Cross reality system for large scale environment reconstruction

ABSTRACT

An extended or cross reality system includes a computing device communicably connected to a plurality of portable electronic devices via a network component, a repository accessible by the computing device and the plurality of portable electronic devices, and a dense map merge component. The extended or cross reality system determines a representation for multiple portions of a 3D environment based at least in part upon on a set of dense maps received from the plurality of portable devices, wherein the set of dense maps is grouped into multiple subgroups based at least in part upon pose data pertaining to the set of dense maps or surface information in the set of dense maps. The extended or cross reality system storing the representation as at least a portion of a shared persistent dense map.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Prov. Pat. App. Ser. No.62/982,694 having the title “CROSS REALITY SYSTEM FOR LARGE SCALEENVIRONMENT RECONSTRUCTION” and filed on Feb. 27, 2020. This applicationis related to U.S. patent application Ser. No. 17/180,453 having thetitle “CROSS REALITY SYSTEM WITH WIFI/GPS BASED MAP MERGE” and filed onFeb. 19, 2021 under Atty. Dkt. No. ML-1016US, International ApplicationNo. PCT/US21/18893 having the title “CROSS REALITY SYSTEM WITH WIFI/GPSBASED MAP MERGE” and filed on Feb. 19, 2021, and U.S. patent applicationSer. No. ______/______,______ having the title “CROSS REALITY SYSTEMWITH BUFFERING FOR LOCALIZATION ACCURACY” and filed concurrently underAtty. Dkt. No. ML-1017US with the present application. The content ofthe aforementioned U.S. patent applications and U.S. provisional patentapplication is hereby explicitly incorporated by reference in itsentirety for all purposes.

TECHNICAL FIELD

This application relates generally to a cross reality system.

BACKGROUND

Computers may control human user interfaces to create a cross reality orextended reality (XR) environment in which some or all of the XRenvironment, as perceived by the user, is generated by the computer.These XR environments may be virtual reality (VR), augmented reality(AR), and mixed reality (MR) environments, in which some or all of an XRenvironment may be generated by computers using, in part, data thatdescribes the environment. This data may describe, for example, virtualobjects that may be rendered in a way that users' sense or perceive as apart of a physical world such that users can interact with the virtualobjects. The user may experience these virtual objects as a result ofthe data being rendered and presented through a user interface device,such as, for example, a head-mounted display device. The data may bedisplayed to the user to see, or may control audio that is played forthe user to hear, or may control a tactile (or haptic) interface,enabling the user to experience touch sensations that the user senses orperceives as feeling the virtual object.

XR systems may be useful for many applications, spanning the fields ofscientific visualization, medical training, engineering design andprototyping, tele-manipulation and tele-presence, and personalentertainment. AR and MR, in contrast to VR, include one or more virtualobjects in relation to real objects of the physical world. Theexperience of virtual objects interacting with real objects greatlyenhances the user's enjoyment in using the XR system, and also opens thedoor for a variety of applications that present realistic and readilyunderstandable information about how the physical world might bealtered.

To realistically render virtual content, an XR system may build arepresentation of the physical world around a user of the system. Thisrepresentation, for example, may be constructed by processing imagesacquired with sensors on a wearable device that forms a part of the XRsystem. In such a system, a user might perform an initialization routineby looking around a room or other physical environment in which the userintends to use the XR system until the system acquires sufficientinformation to construct a representation of that environment. As thesystem operates and the user moves around the environment or to otherenvironments, the sensors on the wearable devices might acquireadditional information to expand or update the representation of thephysical world.

An XR device may reconstruct a representation of the physical worldaround a user by determining, based on sensor data, the location ofsurfaces relative to the XR device. Surfaces in a reconstruction of thephysical world may be represented in one or more formats. One suchformat is a “mesh.” A mesh may be represented by multiple,interconnected triangles. Each triangle has edges joining points on asurface of an object within the physical world, such that each trianglerepresents a portion of the surface. Information about the portion ofthe surface, such as color, texture or other properties may be stored inassociate within the triangle. In operation, an XR device may processimage information to detect points and surfaces so as to create orupdate the mesh. Surfaces may be represented in other ways, such as byplanes or by voxels at locations with respect to the device with valuesassigned to voxels indicating whether a surface was detected between thedevice and the location represented by the voxel.

BRIEF SUMMARY

Aspects of the present application relate to methods and apparatus forproviding cross reality (XR) scenes. Techniques as described herein maybe used together, separately, or in any suitable combination.

Some embodiments relate to an XR system that supports rendering ofvirtual content based on stored maps sharing a coordinate frame. Thestored maps comprise at least a sparse map and a dense map. The sparsemap comprises one or more persistent poses. The dense map comprisesvolumetric data. The system comprises one or more computing devicesconfigured for network communication with a plurality of portableelectronic devices. The one or more computing devices comprise acommunication component configured to receive from each of the one ormore portable electronic devices a collection of posed surfaceinformation. The one or more computing devices comprise a dense mapmerge component configured to compute representations of a plurality ofportions of a 3D environment based on the collections of posed surfaceinformation received from the plurality of portable devices, and storethe representation of at least a portion of the 3D environment as atleast a portion of a stored dense map in the database of stored maps.The representation of each of the plurality of portions is computed fromcollections of posed surface information grouped by poses of the surfaceinformation.

In some embodiments, the communication component is further configuredto receive from each of the one or more portable electronic devices asparse tracking map comprising one or more persistent poses.

In some embodiments, each of the one or more collections of surfaceinformation received from a portable electronic device is posed withrespect to a persistent pose of a respective sparse tracking map of theportable electronic device.

In some embodiments, the one or more computing devices comprise a sparsemap merge component configured to merge the sparse tracking mapsreceived from the one or more portable electronic devices, the mergedsparse map comprising a merged persistent pose table computed based onthe one or more persistent poses of the sparse tracking maps; and asparse map localization component configured to compute a transformationfor a selected sparse tracking maps with respect to the merged sparsemap.

In some embodiments, the sparse map merge component is configured tostore the merged persistent pose table as at least a portion of thestored sparse map in the database of stored maps.

In some embodiments, the dense map merge component comprises a 3Dreconstruction component configured to compute the representation of atleast a portion of the 3D environment based on the merged persistentpose table, and a subset of the one or more collections of surfaceinformation, the subset being selected based on that the subset isassociated with persistent poses in the merged persistent pose table.

In some embodiments, the one or more collections of surface informationeach comprises a depth image.

In some embodiments, the stored dense map comprises metadata comprisinga size of volumetric data contained by the stored dense map, and a mapunique identifier comprising a map universally unique identifier (mapUUID) and a map version identifier.

In some embodiments, each sparse tracking map comprises a map uniqueidentifier comprising a map universally unique identifier (map UUID) anda map version identifier; and a table of the one or more persistentposes. Each persistent pose comprises a pose universally uniqueidentifier (pose UUID), and a pose comprising six degrees of freedom.The table of the one or more persistent poses indicating correspondencesbetween the pose universally unique identifiers and the poses.

In some embodiments, for a same portion of the 3D environment, a densemap for the portion and a sparse map for the portion have a same mapuniversally unique identifier.

In some embodiments, the same map universally unique identifier is128-bit.

In some embodiments, the one or more collections of surface informationcomprise collections of objects information. The dense map mergecomponent comprises a sparse map selection component configured toselect the sparse tracking map based on that the selected sparsetracking map comprises a same map identifier as a dense map comprisingthe collection of objects information.

In some embodiments, the collections of objects information compriseplanar surfaces.

In some embodiments, the representation of at least a portion of the 3Denvironment is segmented in cubics.

In some embodiments, the stored dense map comprises volumetric datacomprising a plurality of voxels. Each voxel comprises a signed distancefunction indicating a distance to a nearest surface in the at least aportion of the 3D environment.

In some embodiments, for individual collections of surface informationassociated with a persistent pose in the merged persistent pose table,the volumetric data is computed separately.

Some embodiments relate to an electronic device configured to operatewithin a cross reality system. The electronic device comprises one ormore sensors configured to capture information about a three-dimensional(3D) environment, the captured information comprising a plurality ofimages; a mapping component configured to compute a sparse tracking mapbased on the plurality of images; a reconstruction component configuredto compute collections of surface information based on the capturedinformation; a communication component configured to, through a network:transmit one or more of the collections of surface information and poseinformation for the collections of surface information, and receivemetadata of a dense map, the metadata indicating a portion of the 3Denvironment represented by the dense map; and at least one processorconfigured to execute computer executable instructions, wherein thecomputer executable instructions comprise instructions for determining,based at least in part on the sparse tracking map and the receivedmetadata, whether to obtain at least a portion of the dense map.

In some embodiments, the dense map is a first dense map. The electronicdevice comprises a filesystem comprising metadata of one or more densemaps.

In some embodiments, for each dense map, the metadata comprises aquality metric indicating a number of mesh blocks in the dense map, anda timestamp indicating the time when a last depth image has been fusedinto the dense map. Determining whether to obtain at least a portion ofthe first dense map is based, at least in part, on the quality metric ofthe first dense map and the quality metrics of the one or more densemaps.

In some embodiments, the computer executable instructions compriseinstructions for: when it is determined to obtain at least a portion ofthe dense map, computing a locally merged map based, at least in part,on the obtained at least a portion of the dense map and locallygenerated collections of surface information, the local merged map beingused for AR functions such as visual occlusion and/or virtual objectsphysics.

In some embodiments, the locally generated collections of surfaceinformation are not represented in the obtained at least a portion ofthe dense map.

In some embodiments, the locally merged dense map comprises sub-regionscorresponding to identified locations.

In some embodiments, the locations are identified based on persistentposes in a sparse tracking map.

In some embodiments, the determining, based at least in part on thesparse tracking map and the received metadata, whether to obtain atleast a portion of the dense map comprises determining an area theelectronic device is moving into based at least in part on a pose of theelectronic device, and downloading surface information of the area whenit is determined that the surface information of the area is availablein the dense map but not on the device.

In some embodiments, the dense map comprises a plurality of sub-regionsassociated with persistent poses in a sparse tracking map, andsub-regions corresponding to the area is downloaded in an order based,at least in part, on distances between the pose of the electronic deviceand the persistent poses associated with the sub-regions.

Some embodiments relate to an electronic device configured to operatewithin a cross reality system. The electronic device comprises one ormore sensors configured to capture information about a three-dimensional(3D) environment, the captured information comprising a plurality ofimages; a mapping component configured to compute a sparse tracking mapbased on the plurality of images; a reconstruction component configuredto compute collections of surface information based on the capturedinformation; a communication component configured to, through a network:transmit one or more of the collections of surface information and poseinformation for the collections of surface information, and receivemetadata of a dense map, the metadata comprising a map ID transfer tableindicating correspondences between unique IDs for objects of the densemap and local IDs of the objects to devices from which the objects wereobtained; and at least one processor configured to execute computerexecutable instructions, wherein the computer executable instructionscomprise instructions for matching the unique IDs for at least a portionof the objects of the dense map to local IDs of corresponding objects tothe electronic device.

In some embodiments, the matching comprises determining, based on themap ID transfer table and a local object ID history, any of the objectsof the dense map that have historical local IDs to the electronicdevice, and generating new local IDs for the objects of the dense mapthat are determined not to have historical local IDs to the electronicdevice.

In some embodiments, the matching comprises for each of the objects ofthe dense map that are determined to have historical local IDs to theelectronic device, removing all historical local IDs except for ahistorical local ID generated most recently.

The foregoing summary is provided by way of illustration and is notintended to be limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 is a sketch illustrating an example of a simplified augmentedreality (AR) scene, according to some embodiments;

FIG. 2 is a sketch of an exemplary simplified AR scene, showingexemplary use cases of an XR system, according to some embodiments;

FIG. 3 is a schematic diagram illustrating data flow for a single userin an AR system configured to provide an experience to the user of ARcontent interacting with a physical world, according to someembodiments;

FIG. 4 is a schematic diagram illustrating an exemplary AR displaysystem, displaying virtual content for a single user, according to someembodiments;

FIG. 5A is a schematic diagram illustrating a user wearing an AR displaysystem rendering AR content as the user moves through a physical worldenvironment, according to some embodiments;

FIG. 5B is a schematic diagram illustrating a viewing optics assemblyand attendant components, according to some embodiments;

FIG. 6A is a schematic diagram illustrating an AR system using a worldreconstruction system, according to some embodiments;

FIG. 6B is a schematic diagram illustrating components of an AR systemthat maintain a model of a passable world, according to someembodiments;

FIG. 7 is a schematic illustration of a tracking map formed by a devicetraversing a path through a physical world;

FIG. 8 is a schematic diagram illustrating a user of a cross reality(XR) system, perceiving virtual content, according to some embodiments;

FIG. 9 is a block diagram of components of a first XR device of the XRsystem of FIG. 8 that transform between coordinate systems, according tosome embodiments;

FIG. 10 is a schematic diagram illustrating an exemplary transformationof origin coordinate frames into destination coordinate frames in orderto correctly render local XR content, according to some embodiments;

FIG. 11 is a top plan view illustrating pupil-based coordinate frames,according to some embodiments;

FIG. 12 is a top plan view illustrating a camera coordinate frame thatincludes all pupil positions, according to some embodiments;

FIG. 13 is a schematic diagram of the display system of FIG. 9,according to some embodiments;

FIG. 14 is a block diagram illustrating the creation of a persistentcoordinate frame (PCF) and the attachment of XR content to the PCF,according to some embodiments;

FIG. 15 is a flow chart illustrating a method of establishing and usinga PCF, according to some embodiments;

FIG. 16 is a block diagram of the XR system of FIG. 8, including asecond XR device, according to some embodiments;

FIG. 17 is a schematic diagram illustrating a room and key frames thatare established for various areas in the room, according to someembodiments;

FIG. 18 is a schematic diagram illustrating the establishment ofpersistent poses based on the key frames, according to some embodiments;

FIG. 19 is a schematic diagram illustrating the establishment of apersistent coordinate frame (PCF) based on the persistent poses,according to some embodiments;

FIGS. 20A to 20C are schematic diagrams illustrating an example ofcreating PCFs, according to some embodiments;

FIG. 21 is a block diagram illustrating a system for generating globaldescriptors for individual images and/or maps, according to someembodiments;

FIG. 22 is a flow chart illustrating a method of computing an imagedescriptor, according to some embodiments;

FIG. 23 is a flow chart illustrating a method of localization usingimage descriptors, according to some embodiments;

FIG. 24 is a flow chart illustrating a method of training a neuralnetwork, according to some embodiments;

FIG. 25 is a block diagram illustrating a method of training a neuralnetwork, according to some embodiments;

FIG. 26 is a schematic diagram illustrating an AR system configured torank and merge a plurality of environment maps, according to someembodiments;

FIG. 27 is a simplified block diagram illustrating a plurality ofcanonical maps stored on a remote storage medium, according to someembodiments;

FIG. 28 is a schematic diagram illustrating a method of selectingcanonical maps to, for example, localize a new tracking map in one ormore canonical maps and/or obtain PCFs from the canonical maps,according to some embodiments;

FIG. 29 is flow chart illustrating a method of selecting a plurality ofranked environment maps, according to some embodiments;

FIG. 30 is a schematic diagram illustrating an exemplary map rankportion of the AR system of FIG. 26, according to some embodiments;

FIG. 31A is a schematic diagram illustrating an example of areaattributes of a tracking map (TM) and environment maps in a database,according to some embodiments;

FIG. 31B is a schematic diagram illustrating an example of determining ageographic location of a tracking map (TM) for geolocation filtering ofFIG. 29, according to some embodiments;

FIG. 32 is a schematic diagram illustrating an example of geolocationfiltering of FIG. 29, according to some embodiments;

FIG. 33 is a schematic diagram illustrating an example of Wi-Fi BSSIDfiltering of FIG. 29, according to some embodiments;

FIG. 34 is a schematic diagram illustrating an example of use oflocalization of FIG. 29, according to some embodiments;

FIGS. 35 and 36 are block diagrams of an XR system configured to rankand merge a plurality of environment maps, according to someembodiments.

FIG. 37 is a block diagram illustrating a method of creating environmentmaps of a physical world, in a canonical form, according to someembodiments;

FIGS. 38A and 38B are schematic diagrams illustrating an environment mapcreated in a canonical form by updating the tracking map of FIG. 7 witha new tracking map, according to some embodiments.

FIGS. 39A to 39F are schematic diagrams illustrating an example ofmerging maps, according to some embodiments;

FIG. 40 is a two-dimensional representation of a three-dimensional firstlocal tracking map (Map 1), which may be generated by the first XRdevice of FIG. 9, according to some embodiments;

FIG. 41 is a block diagram illustrating uploading Map 1 from the firstXR device to the server of FIG. 9, according to some embodiments;

FIG. 42 is a schematic diagram illustrating the XR system of FIG. 16,showing the second user has initiated a second session using a second XRdevice of the XR system after the first user has terminated a firstsession, according to some embodiments;

FIG. 43A is a block diagram illustrating a new session for the second XRdevice of FIG. 42, according to some embodiments;

FIG. 43B is a block diagram illustrating the creation of a tracking mapfor the second XR device of FIG. 42, according to some embodiments;

FIG. 43C is a block diagram illustrating downloading a canonical mapfrom the server to the second XR device of FIG. 42, according to someembodiments;

FIG. 44 is a schematic diagram illustrating a localization attempt tolocalize to a canonical map a second tracking map (Map 2), which may begenerated by the second XR device of FIG. 42, according to someembodiments;

FIG. 45 is a schematic diagram illustrating a localization attempt tolocalize to a canonical map the second tracking map (Map 2) of FIG. 44,which may be further developed and with XR content associated with PCFsof Map 2, according to some embodiments;

FIGS. 46A-46B are a schematic diagram illustrating a successfullocalization of Map 2 of FIG. 45 to the canonical map, according to someembodiments;

FIG. 47 is a schematic diagram illustrating a canonical map generated byincluding one or more PCFs from the canonical map of FIG. 46A into Map 2of FIG. 45, according to some embodiments;

FIG. 48 is a schematic diagram illustrating the canonical map of FIG. 47with further expansion of Map 2 on the second XR device, according tosome embodiments;

FIG. 49 is a block diagram illustrating uploading Map 2 from the secondXR device to the server, according to some embodiments;

FIG. 50 is a block diagram illustrating merging Map 2 with the canonicalmap, according to some embodiments;

FIG. 51 is a block diagram illustrating transmission of a new canonicalmap from the server to the first and second XR devices, according tosome embodiments;

FIG. 52 is block diagram illustrating a two-dimensional representationof Map 2 and a head coordinate frame of the second XR device that isreferenced to Map 2, according to some embodiments;

FIG. 53 is a block diagram illustrating, in two-dimensions, adjustmentof the head coordinate frame which can occur in six degrees of freedom,according to some embodiments;

FIG. 54 a block diagram illustrating a canonical map on the second XRdevice wherein sound is localized relative to PCFs of Map 2, accordingto some embodiments;

FIGS. 55 and 56 are a perspective view and a block diagram illustratinguse of the XR system when the first user has terminated a first sessionand the first user has initiated a second session using the XR system,according to some embodiments;

FIGS. 57 and 58 are a perspective view and a block diagram illustratinguse of the XR system when three users are simultaneously using the XRsystem in the same session, according to some embodiments;

FIG. 59 is a flow chart illustrating a method of recovering andresetting a headpose, according to some embodiments;

FIG. 60 is a block diagram of a machine in the form of a computer thatcan find application in the present invention system, according to someembodiments;

FIG. 61 is a schematic diagram of an example XR system in which any ofmultiple devices may access a localization service, according to someembodiments;

FIG. 62 is an example process flow for operation of a portable device aspart of an XR system that provides cloud-based localization, accordingto some embodiments;

FIGS. 63A, B, and C are an example process flow for cloud-basedlocalization, according to some embodiments;

FIG. 64 is a block diagram of an XR system that provides 3Dreconstruction for large scale environments, according to someembodiments;

FIG. 65A is a block diagram of a cloud sparse map merge component of theXR system of FIG. 64, according to some embodiments;

FIG. 65B is a visualization diagram illustrating some information of amerged shared sparse map provided by the cloud sparse map mergecomponent of FIG. 65A, according to some embodiments;

FIG. 65C is another visualization diagram illustrating some informationof a merged shared sparse map of FIG. 65B, according to someembodiments;

FIG. 66 is a block diagram of an example of the cloud dense map mergecomponent of the XR system of FIG. 64, according to some embodiments;

FIG. 67 is block diagram of another example of the cloud dense map mergecomponent of the XR system of FIG. 64, according to some embodiments;

FIG. 68 is a simplified schematic diagram illustrating a merged shareddense map provided by the cloud dense map merge component of the XRsystem of FIG. 64, according to some embodiments;

FIG. 69 is a block diagram of an example of the on-device dense maphandling component of the XR system of FIG. 64, according to someembodiments;

FIG. 70 is a flow chart illustrating a method of on-device dense mappre-fetch for getting dense map data from the cloud-stored merged shareddense map to device, according to some embodiments;

FIG. 71 is a simplified schematic diagram illustrating obtaining thecloud-stored merged shared dense map of FIG. 68 using the method of FIG.70, according to some embodiments;

FIG. 72 is flow chart illustrating a method of merging dense maps on anXR device, according to some embodiments;

FIG. 73 is a block diagram of an XR system that provides persistentplanes, according to some embodiments; and

FIG. 74 is a block diagram of an XR system that provides persistentplanes, according to some embodiments.

DETAILED DESCRIPTION

Described herein are methods and apparatus for providing XR scenes. Toprovide realistic XR experiences to multiple users, an XR system mayprovide information on users' locations within the physical world andthe shape and location of objects within the physical world. Suchinformation may enable the system to correctly correlate locations ofvirtual objects in relation to real objects. The inventors haverecognized and appreciated methods and apparatus that generate and share3D representations of large and very large-scale environments (e.g., aneighborhood, a city, a country, the globe) with computation resourcesand network bandwidth suitable for portable devices including, forexample, AR system 580 (FIG. 4), XR device 12.1 (FIG. 8), andsmartphones.

An XR system may build representations of a 3D environment, which may becreated from image and/or depth information collected with sensors thatare part of XR devices worn by users of the XR system. The 3Denvironment representations may be used by any components of XR devicesin the XR system. For example, the 3D environment representation may beused by components that perform visual occlusion processing, computephysics-based interactions, or perform environmental reasoning.

Occlusion processing identifies portions of a virtual object that shouldnot be rendered for and/or displayed to a user because there is anobject in the physical world blocking that user's view of the locationwhere that virtual object is to be perceived by the user. Physics-basedinteractions are computed to determine where and/or how a virtual objectappears to the user. For example, a virtual object may be rendered so asto appear to be resting on a physical object, moving through empty spaceor colliding with a surface of a physical object.

Environmental reasoning may also use the 3D environment representationsin the course of generating information that can be used in computinghow to render virtual objects. For example, environmental reasoning mayinvolve identifying clear surfaces by recognizing that they are windowpanes or glass table tops. From such an identification, regions thatcontain physical objects might be classified as not occluding virtualobjects but might be classified as interacting virtual objects.Environmental reasoning may also generate information used in otherways, such as identifying stationary objects that may be trackedrelative to a user's field of view to compute motion of the user's fieldof view.

The 3D environment representations provide a model from whichinformation about objects in the physical world may be obtained for suchcalculations. However, there are significant challenges in providingsuch a system that provides a real-time, immersive XR experience.Substantial processing may be required to compute the 3D environmentrepresentations. Further, the 3D environment representations are oftenrequired to be updated as objects move in the physical world (e.g., acup moves on a table). Updates to the data representing the environmentthat the user is experiencing must be performed quickly without using somuch of the computing resources of the device generating the XRenvironment because the device may be unable to perform other functionswhile generating and updating the 3D environment representations.

The inventors have realized and appreciated an XR system enables any ofmultiple devices to efficiently and accurately access previouslypersisted representations of very large-scale environments and rendervirtual content specified in relation to those representations. Sharedcomputing resources accessible to multiple devices over a network, suchas a cloud service, may generate and store 3D representations oflarge-scale environments using data captured by one or more devices, andenable any device in the XR system to access the persisted 3Drepresentations.

The persisted 3D representations may be divided into smaller volumessuch that a device can quickly access the volumes visible from thedevice's position, with a network bandwidth suitable for the device. Adevice in the XR system assessing persisted 3D representations mayupdate the persisted 3D representations with fresh data captured by thedevice such that the device has 3D representations reflecting anup-to-date physical world geometry. A device may manage the smallervolumes of such that a suitable 3D representation can be accessed withlow latency and low computational overhead. For example, some of thesmaller volumes of the 3D representations may be stored in a filesystemfor future use, for example, when the device re-enters a previouslyexplored space. Further, the 3D representations may be formatted tofacilitate combining and persisting information on surfaces in the 3Denvironment. For example, the surface information may include objectinformation indicating locations of real objects, such as planes,identifiable by unique descriptors such that virtual content may bepersisted by being associated with the object information. Alternativelyor additionally, surfaces may be represented by meshes or by volumetricdata, such as voxels, for example.

Various embodiments described herein employ one or more of maps that mayinclude, without limitation, one or more sparse maps, one or more densemaps, one or more tracking maps, one or more canonical maps, and/or oneor more environment maps.

A tracking map may be local to the device (e.g., an XR device) thatoriginally created or subsequently updated the tracking map. A trackingmap may serve as a dense map although a tracking map may start as asparse map. A tracking map may include data such as headpose data of adevice (e.g., the location, orientation, and/or pose of an XR devicecreating or updating the tracking map) in some embodiments. Such datamay be represented as a point or a point node in a tracking map in someof these embodiments.

In some of these embodiments, a tracking map may further include surfaceinformation or data that may be represented as a set of meshes or depthinformation and/or other high-level data (e.g., location and/or one ormore characteristics pertaining to one or more planes or surfaces orother objects) that may be derived from the surface or depthinformation. A tracking map may be promoted to a canonical map whichwill be described in greater details below. In some embodiments, atracking map provides a floorplan of physical objects in the physicalworld. For example, a physical object or a feature thereof (e.g., avertex, an edge, a plane or surface, etc. determined from imageprocessing) may be represented as a point or point node in a trackingmap.

In some of these embodiments, a tracking map may include data pertainingto a point or point node. Such data may include, for example, absoluteand/or relative poses (e.g., absolute location, orientation, and/or gazedirection relative to a known, fixed reference, relative location,orientation, and/or gaze direction relative to an XR device at aparticular location, orientation, and/or gaze direction). A featurerepresenting a physical object or a portion thereof may be derived from,for example, image processing of one or more images containing thephysical object or the portion thereof and may be used as a persistentpose that in turn may be transformed into a persistent coordinate frame(PCF). In some embodiments, a persistent coordinate frame comprises alocal coordinate frame (e.g., a coordinate frame local to a referencepoint or coordinate system of an XR device) that allows contentpersistence—placement of digital contents in a virtual or mixed-realityenvironment and have the placed digital contents stay in the samelocation(s) in, for example, the virtual- or mixed-reality environment(e.g., a passable world model, a shared world model, and/or one or moremaps described herein), without drifts or deviations beyond a predefinedthreshold across multiple user sessions (for one or more XR devices)even after closing, re-opening an application or rebooting of XRdevice(s).

A PCF may be placed in, for example, a canonical map described herein ata specific location, in a specific orientation, and/or in a particulargaze direction from the perspective of the viewing XR device that firstcreates the PCF to represent an object or a portion thereof. When an XRdevice enters a physical environment that the XR device or anothercollaborating XR device has already seen before (e.g., via one or moreimages captured by the XR device or another collaborating XR device),the persistent coordinate frames placed for this physical environmentmay be restored in the correct location(s) by the XR device byretrieving one or more corresponding canonical maps created for at leasta portion of the physical environment.

In some embodiments, a PCF corresponds to a predefined position in thephysical world and has a unique identifier which a user may store in abrowser session, on the portable computing device (e.g., an XR device),or on a remote server. This unique identifier may be shared amongmultiple users. For example, when a digital content is placed in avirtual- or mixed-reality session, the nearer or nearest PCF may berequested, and the unique identifier of the nearer or the nearest PCF aswell as the location corresponding to the PCF may be stored.

When the PCF is reused in a different session for a user at a differentlocation, this stored location corresponding to the PCF may betransformed (e.g., translation, rotation, mirroring, etc.) with respectto the current coordinate system for the different location for thedifferent virtual- or mixed-reality session so that the digital contentis placed at the correct location relative to the user in the differentsession. A PCF may store persistent spatial information in someembodiments. In some of these embodiments, a PCF may further include atransformation relative to a reference location, orientation, and/orgaze direction as well as information derived from one or more images ata location that corresponds to the PCF. For example, a PCF may includeor may be at least associated with a transformation between a coordinateframe of a map (e.g., a tracking map, a canonical map, an environmentmap, a sparse map, and/or a dense map, etc.) and the PCF.

In some of these embodiments, a PCF may include geographic or spatialinformation indicating a location within a 3D environment of a keyframeor image frame from which the persistent coordinate frame is created. Insome embodiments, the transformation may be determined between acoordinate frame local to the portable computing device (e.g., an XRdevice) and a stored coordinate frame.

In some embodiments, all PCFs are sharable and can be transmitted amongmultiple users at respective, different locations. In some otherembodiments, one or more PCFs are only known to an XR device that firstcreated these one or more PCFs. In some embodiments, information aboutthe physical world, for example, may be represented as persistentcoordinate frames (PCFs). A PCF may be defined based on one or morepoints that represent features recognized in the physical world. Thefeatures may be selected such that they are likely to be the same fromuser session to user session of the XR system. PCFs may exist sparsely,providing less than all of the available information about the physicalworld, such that they may be efficiently processed and transferred.Techniques for processing persistent spatial information may includecreating dynamic maps based on one or more coordinate systems in realspace across one or more sessions, and generating persistent coordinateframes (PCF) over the sparse maps, which may be exposed to XRapplications via, for example, an application programming interface(API). These capabilities may be supported by techniques for ranking andmerging or stitching multiple maps created by one or more XR devices.Persistent spatial information may also enable quickly recovering andresetting head poses on each of one or more XR devices in acomputationally efficient way. In some embodiments, an XR system mayassign a coordinate frame to a virtual content, as opposed to attachingthe virtual content in a world coordinate frame. Such configurationenables a virtual content to be described without regard to where it isrendered for a user, but it may be attached to a more persistent frameposition such as a persistent coordinate frame (PCF) to be rendered in aspecified location. When the locations of the objects change, the XRdevice may detect the changes in the environment map and determinemovement of the head unit worn by the user relative to real-worldobjects.

In some embodiments, spatial persistence may be provided throughpersistent coordinate frames (PCFs). A PCF may be defined based on oneor more points, representing features recognized in the physical world(e.g., corners, edges). The features may be selected such that they arelikely to be the same from a user instance to another user instance ofan XR system. In addition or in the alternative, drift during tracking,which causes the computed tracking path (e.g., camera trajectory) todeviate from the actual tracking path, can cause the location of virtualcontent, when rendered with respect to a local map that is based solelyon a tracking map to appear out of place. A tracking map for the spacemay be refined to correct the drifts as an XR device collects moreinformation of the scene overtime. However, if virtual content is placedon a real object before a map refinement and saved with respect to theworld coordinate frame of the device derived from the tracking map, thevirtual content may appear displaced, as if the real object has beenmoved during the map refinement. PCFs may be updated according to maprefinement because the PCFs are defined based on the features and areupdated as the features move during map refinements.

In some embodiments, a PCF may comprise six degrees of freedom withtranslations and rotations relative to a map coordinate system. A PCFmay be stored in a local and/or remote storage medium. The translationsand rotations of a PCF may be computed relative to a map coordinatesystem depending on, for example, the storage location. For example, aPCF used locally by a device may have translations and rotationsrelative to a world coordinate frame of the device. A PCF in the cloudmay have translations and rotations relative to a canonical coordinateframe of a canonical map. In some embodiments, PCFs may provide a sparserepresentation of the physical world, providing less than all of theavailable information about the physical world, such that they may beefficiently processed and transferred. Techniques for processingpersistent spatial information may include creating dynamic maps basedon one or more coordinate systems in real space across one or moresessions, generating persistent coordinate frames (PCF) over the sparsemaps, which may be exposed to XR applications via, for example, anapplication programming interface (API).

In some embodiments, a tracking map may include one or more imageframes, keyframes, etc. and/or data derived from one or more imageframes, keyframes, etc. It shall be noted that not all features andimage frames (or keyframes) may be retained as a part of a tracking map.Rather, feature points or image frames that provide meaningfulinformation (e.g., non-redundant information, information withsufficiently high accuracy and/or resolution beyond a predefinedthreshold value, etc.) may be retained in a tracking map. Therefore, atracking map may be constructed with data collected or gathered by oneor more cameras, image sensors, depth sensors, GPS (global positioning)devices, wireless devices (e.g., a Wi-Fi or a cellular transceiver),etc.

A tracking map may be stored as or merged or stitched with anenvironment map which will also be described in greater details below. Atracking map may be refined to correct, for example, any deviations ordrifts of a computed tracking path (e.g., a computed camera trajectory)that deviates from the actual tracking path. In some embodiments, imagesproviding meaningful information to a tracking map may be selected askeyframes that may further be integrated within (e.g., embedded within atracking map) or associated with (e.g., stored separated yet linked witha tracking map) a tracking map. In some embodiments, a tracking map maybe connected to or associated with a pose and a PCF (persistentcoordinate frame) transformer.

A sparse map may include data that indicates a location of a point orstructure of interest (e.g., a corner, an edge, a surface, etc.) insteadof all the locations of features in some embodiments. That is, certainpoints or structures in a physical environment may be discarded from asparse map. A sparse map may be constructed with a set of mapped pointsand/or keyframes of interest by, for example, image processing thatextracts one or more points or structures of interest. One example of asparse may include a tracking map described above. In this example, thetracking map may be deemed as a headpose sparse map although it shall benoted that other sparse maps may not always include headpose data. Asparse map may include information or data that may be used to derive acoordinate system in some embodiments. In some other embodiments, asparse map may include or may be associated with a coordinate system.Such a coordinate system may be used to define the position and/ororientation of an object or a feature thereof in a dense map that willbe described in greater details below.

A dense map may include, for example, data or information such assurface data represented by mesh or depth information in someembodiments. In some of these embodiments, a dense map may includehigh-level information that may be derived from surface or depthinformation. For example, a dense map may include data pertaining to thelocation and/or one or more characteristics of a plane and/or otherobject(s). A dense map may be augmented from a sparse map by augmentingthe original data of the sparse map with any of the aforementioned dataor information although it shall be noted that sparse maps may becreated independently of the creation of dense maps, and vice versa, insome embodiments.

An environment map may include data or information of a piece of aphysical world. For example, an environment may include locations,orientations, and/or gaze directions (when perceived with an XR device)of physical objects in a physical environment (e.g., a portion of aroom) as well as other data pertaining to the physical objects (e.g.,surface textures, colors, etc.) An environment map may be created withdata from, for example, images from camera(s) and/or depth data fromdepth sensor(s). An environment may thus include much more details aboutthe physical environment than any other maps described herein and may beused to construct a passable 3D world that may be collaborated upon(e.g., created by multiple XR devices moving around the physical world)and shared among multiple XR devices. An environment map may betransformed or stripped into a canonical map by, for example, removingunneeded data or information in some embodiments. An environment map mayalso be associated with multiple area or volume attributes such as oneor more parameters, strengths of received signals, etc. of wirelessnetworks, or any other desired or required parameters.

A canonical map may include data or information so that the canonicalmap may be localized and oriented to each of a plurality of computingdevices so that each computing device may reuse the canonical map. Acanonical map may thus originate as a tracking map or a sparse in someembodiments or as an environment map in some other environment.

A canonical map may include or may be associated with just enough datathat determines a location of an object represented in the canonical mapin some embodiments. For example, a canonical map may include or may beassociated with persistent pose(s) and/or persistent coordinate frame(s)of an object of interest or a portion thereof. A canonical map, like anyother maps described herein, may be merged or stitched with, forexample, the persistent coordinate frame(s) in another map to render anew canonical map (e.g., by using a map merge or stitch algorithm). Insome embodiments, a canonical map includes one or more structures (e.g.,objects) that include one or more persistent coordinate frames (PCFs)that are stored within the canonical map or are otherwise storedseparately in a data structure and are associated with the one or morestructures. A structure (e.g., object) represented in a canonical mapmay include a single PCF node having the persistent coordinate frameinformation and representing an object in some embodiments. In someother embodiments, a structure represented in a canonical map mayinclude multiple PCFs each of which is a local coordinate frame relativeto, for example, a reference system of coordinates of a device thatgenerated or updated the canonical map. Moreover, these multiple PCFsmay respectively represent multiple corresponding features of theobject. In some embodiments, the morphism or one or more functionsdefined for and/or included in a canonical frame may further compriseone or more operations that receive an input (e.g., input location,orientation, pose, surface, coordinate system, and/or depth information)from, for example, a tracking map, a sparse map, a dense map, etc. andgenerate an output for the input by performing a transformation (e.g., amatrix operation for translation, rotation, mirroring, etc.) In someembodiments, a canonical map includes only a morphism (or one or morefunctions) and one or more structure each represented by one or morepersistent coordinate frames but no other data or information. In someother embodiments, a canonical map includes a morphism, one or morestructures each represented by one or more persistent coordinate frames,and data or metadata corresponding to a description or characteristic ofthe structure. More details about a persistent coordinate frame arefurther described below.

A canonical map that has been localized to a specific XR device may bereferred to as a promoted map. In some embodiments, a canonical mapincludes coordinate information (e.g., coordinates of a point, acoordinate system, etc.) and may also include one or more structuresthat include, for example, at least one PCF (persistent coordinateframe). For localizing a tracking map, an image frame or a keyframepertaining to the tracking map may be associated with the at least onePCF pertaining to the canonical map; and the pose of the image may thenbe used to localize the tracking map that stores the pose information.

Canonical maps may be ranked with respective rankings that may indicatecanonical maps that have regions similar to a region of the tracking mapsuch that, upon attempting to merge or stitch the tracking map into thecanonical map, it is like that there will be a correspondence between atleast a portion of the tracking map and at least a portion of thecanonical map. A canonical map may have one or more tiles or definedareas; and merging or stitching the canonical map with another map maybe limited to one or more tiles or defined areas to conserve computingresource consumption.

A canonical map may include a set of features that defines one or morepersistent coordinate frames (PCFs) in some embodiments although thereis no requirement that the set of features be associated with a singlepersistent location in either the canonical map or the tracking map. Insome embodiments, a transformation of the set of feature points in thetracking map to align with the candidate set of features in thecanonical map. This transformation may be applied to the entire trackingmap, enabling overlapping portions of the tracking map and the canonicalmap to be identified.

Multiple canonical maps may be merged or stitched to render a newcanonical map. In some embodiments, the attributes of a canonical mapmay be derived from the attributes of a tracking map or maps used toform an area of the canonical map. In some embodiments in which apersistent coordinate frame of a canonical map is defined based on apersistent pose in a tracking map that was merged or stitched into thecanonical map, the persistent coordinate frame may be assigned the sameattributes as the persistent pose. In some embodiments, a canonical mapmay include a plurality attributes serving as canonical map identifiersindicating the canonical map's location within a physical space, such assomewhere on the planet earth or in the space.

In some embodiments, multiple canonical maps may be disposedgeographically in a two-dimensional pattern as these multiple canonicalmaps may exist on a surface of the earth (or in the space). Thesecanonical maps may be uniquely identifiable by, for example,corresponding longitudes and latitudes or positions relative to earth.In these embodiments, these canonical maps may provide a floorplan ofreconstructed physical objects in a corresponding physical world,represented by respective points. A map point in a canonical map mayrepresent a feature of a physical object that may include multiplefeatures. In some embodiments where a server stores no canonical map fora region of the physical world represented by the tracking map, thetracking map may be stored as an initial canonical map that may furtherbe processed to become a canonical map having the pertinent data, whenavailable.

In an XR system, each XR device may develop a local dense map of itsphysical environment by integrating information from one or more imagescollected as the device operates. The local dense map may include 3Drepresentations of the environment in one or more forms including, forexample, voxels, meshes, or planes. U.S. patent application Ser. No.16/229,799 describes generating 3D representations on device and ishereby incorporated herein by reference it its entirety.

In some embodiments, dense information, regardless of its format, may beposed with respect to a coordinate frame defined in a sparse map. Adevice, for example, may maintain a local sparse tracking map, which maybe constructed with sets of features, forming persistent poses in thetracking map. The location and orientation of a surface, for example,may be expressed relative to such a persistent pose.

The local coordinate frames defined by sparse maps in each device may berelated to each other through a shared frame of reference provided by ashared sparse map. Such a shared sparse map may be formed by merging orstitching tracking maps from multiple devices into larger sparse mapsthat are shared across multiple devices. Each device may localize itsposition, expressed relative to its tracking map, with respect to ashared sparse map—enabling each of multiple devices to use its localtracking maps to identify a location and orientation in the 3Denvironment specified in a coordinate frame of the shared map.

The devices may use shared location information derived through sparsemaps for defining the pose of dense information, such that denseinformation may be spatially correlated. With such spatial correlationdense information may be aggregated from multiple devices and sharedwith multiple devices, each of which may have a different localcoordinate frame.

The XR system may implement one or more techniques so as to enableoperation based on spatial information provided by shared sparse maps.The shared spatial information may be represented by a persistent map.The persistent map may be stored in a remote storage medium (e.g., acloud). For example, the wearable device worn by a user, after beingturned on, may retrieve from persistent storage, such as from cloudstorage, an appropriate map that was previously created and stored. Thatpreviously stored map may have been based on data about the environmentcollected with sensors on the user's wearable device during priorsessions. Retrieving a stored map may enable use of the wearable devicewithout completing a scan of the physical world with the sensors on thewearable device. Alternatively or additionally, the system/device, uponentering a new region of the physical world, may similarly retrieve anappropriate stored map.

The stored map may be represented in a canonical form to which a localframe of reference on each XR device may be related. In a multidevice XRsystem, the stored map accessed by one device may have been created andstored by another device and/or may have been constructed by aggregatingdata about the physical world collected by sensors on multiple wearabledevices that were previously present in at least a portion of thephysical world represented by the stored map.

In some embodiments, persistent spatial information may be representedin a way that may be readily shared among users and among thedistributed components, including applications. Canonical maps mayprovide information about the physical world, for example, as persistentcoordinate frames (PCFs). A PCF may be defined based on a set offeatures recognized in the physical world. The features may be selectedsuch that they are likely to be the same from user session to usersession of the XR system. PCFs may exist sparsely, providing less thanall of the available information about the physical world, such thatthey may be efficiently processed and transferred. Techniques forprocessing persistent spatial information may include creating dynamicmaps based on the local coordinate systems of one or more devices acrossone or more sessions. These maps may be sparse maps, representing thephysical world based on a subset of the feature points detected inimages used in forming the maps. The persistent coordinate frames (PCF)may be generated from the sparse maps, and may be exposed to XRapplications via, for example, an application programming interface(API). These capabilities may be supported by techniques for forming thecanonical maps by merging or stitching multiple maps created by one ormore XR devices.

The relationship between the canonical map and a local map for eachdevice may be determined through a localization process. Thatlocalization process may be performed on each XR device based on a setof canonical maps selected and sent to the device. Alternatively oradditionally, a localization service may be provided on remoteprocessors, such as might be implemented in the cloud.

To support these and other functions, the XR system may includecomponents that, based on data about the physical world collected withsensors on user devices, develop, maintain, and use persistent spatialinformation, including one or more stored maps. These components may bedistributed across the XR system, with some operating, for example, on ahead mounted portion of a user device. Other components may operate on acomputer, associated with the user coupled to the head mounted portionover a local or personal area network. Yet others may operate at aremote location, such as at one or more servers accessible over a widearea network.

Sharing data about the physical world among multiple devices may enableshared user experiences of virtual content. Two XR devices that haveaccess to the same stored map, for example, may both localize withrespect to the stored map. Once localized, a user device may rendervirtual content that has a location specified by reference to the storedmap by translating that location to a frame of reference maintained bythe user device. The user device may use this local frame of referenceto control the display of the user device to render the virtual contentin the specified location.

When the stored maps include dense maps that are posed with respect tothe sparse information, a device may efficiently obtain denseinformation, representing surfaces in the 3D environment. The devicemay, in connection with its motion through the physical world, which maybe tracked through sparse maps, access the stored maps to maintain anup-to-date dense representation of the 3D environment.

Techniques as described herein may be used together or separately withmany types of devices and for many types of scenes, including wearableor portable devices with limited computational resources that provide anaugmented or mixed reality scene. In some embodiments, the techniquesmay be implemented by one or more services that form a portion of an XRsystem.

AR System Overview

FIGS. 1 and 2 illustrate scenes with virtual content displayed inconjunction with a portion of the physical world. For purposes ofillustration, an AR system is used as an example of an XR system. FIGS.3-6B illustrate an exemplary AR system, including one or moreprocessors, memory, sensors and user interfaces that may operateaccording to the techniques described herein.

Referring to FIG. 1, an outdoor AR scene 354 is depicted in which a userof an AR technology sees a physical world park-like setting 356,featuring people, trees, buildings in the background, and a concreteplatform 358. In addition to these items, the user of the AR technologyalso perceives that they “see” a robot statue 357 standing upon thephysical world concrete platform 358, and a cartoon-like avatarcharacter 352 flying by which seems to be a personification of a bumblebee, even though these elements (e.g., the avatar character 352, and therobot statue 357) do not exist in the physical world. Due to the extremecomplexity of the human visual perception and nervous system, it ischallenging to produce an AR technology that facilitates a comfortable,natural-feeling, rich presentation of virtual image elements amongstother virtual or physical world imagery elements.

Such an AR scene may be achieved with a system that builds maps of thephysical world based on tracking information, enable users to place ARcontent in the physical world, determine locations in the maps of thephysical world where AR content are placed, preserve the AR scenes suchthat the placed AR content can be reloaded to display in the physicalworld during, for example, a different AR experience session, and enablemultiple users to share an AR experience. The system may build andupdate a digital representation of the physical world surfaces aroundthe user. This representation may be used to render virtual content soas to appear fully or partially occluded by physical objects between theuser and the rendered location of the virtual content, to place virtualobjects, in physics-based interactions, and for virtual character pathplanning and navigation, or for other operations in which informationabout the physical world is used.

FIG. 2 depicts another example of an indoor AR scene 400, showingexemplary use cases of an XR system, according to some embodiments. Theexemplary scene 400 is a living room having walls, a bookshelf on oneside of a wall, a floor lamp at a corner of the room, a floor, a sofa,and coffee table on the floor. In addition to these physical items, theuser of the AR technology also perceives virtual objects such as imageson the wall behind the sofa, birds flying through the door, a deerpeeking out from the book shelf, and a decoration in the form of awindmill placed on the coffee table.

For the images on the wall, the AR technology requires information aboutnot only surfaces of the wall but also objects and surfaces in the roomsuch as lamp shape, which are occluding the images to render the virtualobjects correctly. For the flying birds, the AR technology requiresinformation about all the objects and surfaces around the room forrendering the birds with realistic physics to avoid the objects andsurfaces or bounce off them if the birds collide. For the deer, the ARtechnology requires information about the surfaces such as the floor orcoffee table to compute where to place the deer. For the windmill, thesystem may identify that is an object separate from the table and maydetermine that it is movable, whereas corners of shelves or corners ofthe wall may be determined to be stationary. Such a distinction may beused in determinations as to which portions of the scene are used orupdated in each of various operations.

The virtual objects may be placed in a previous AR experience session.When new AR experience sessions start in the living room, the ARtechnology requires the virtual objects being accurately displayed atthe locations previously placed and realistically visible from differentviewpoints. For example, the windmill should be displayed as standing onthe books rather than drifting above the table at a different locationwithout the books. Such drifting may happen if the locations of theusers of the new AR experience sessions are not accurately localized inthe living room. As another example, if a user is viewing the windmillfrom a viewpoint different from the viewpoint when the windmill wasplaced, the AR technology requires corresponding sides of the windmillbeing displayed.

A scene may be presented to the user via a system that includes multiplecomponents, including a user interface that can stimulate one or moreuser senses, such as sight, sound, and/or touch. In addition, the systemmay include one or more sensors that may measure parameters of thephysical portions of the scene, including position and/or motion of theuser within the physical portions of the scene. Further, the system mayinclude one or more computing devices, with associated computerhardware, such as memory. These components may be integrated into asingle device or may be distributed across multiple interconnecteddevices. In some embodiments, some or all of these components may beintegrated into a wearable device.

FIG. 3 depicts an AR system 502 configured to provide an experience ofAR contents interacting with a physical world 506, according to someembodiments. The AR system 502 may include a display 508. In theillustrated embodiment, the display 508 may be worn by the user as partof a headset such that a user may wear the display over their eyes likea pair of goggles or glasses. At least a portion of the display may betransparent such that a user may observe a see-through reality 510. Thesee-through reality 510 may correspond to portions of the physical world506 that are within a present viewpoint of the AR system 502, which maycorrespond to the viewpoint of the user in the case that the user iswearing a headset incorporating both the display and sensors of the ARsystem to acquire information about the physical world.

AR contents may also be presented on the display 508, overlaid on thesee-through reality 510. To provide accurate interactions between ARcontents and the see-through reality 510 on the display 508, the ARsystem 502 may include sensors 522 configured to capture informationabout the physical world 506.

The sensors 522 may include one or more depth sensors that output depthmaps 512. Each depth map 512 may have multiple pixels, each of which mayrepresent a distance to a surface in the physical world 506 in aparticular direction relative to the depth sensor. Raw depth data maycome from a depth sensor to create a depth map. Such depth maps may beupdated as fast as the depth sensor can form a new image, which may behundreds or thousands of times per second. However, that data may benoisy and incomplete, and have holes shown as black pixels on theillustrated depth map.

The system may include other sensors, such as image sensors. The imagesensors may acquire monocular or stereoscopic information that may beprocessed to represent the physical world in other ways. For example,the images may be processed in world reconstruction component 516 tocreate a mesh, representing connected portions of objects in thephysical world. Metadata about such objects, including for example,color and surface texture, may similarly be acquired with the sensorsand stored as part of the world reconstruction.

The system may also acquire information about the headpose (or “pose”)of the user with respect to the physical world. In some embodiments, aheadpose tracking component of the system may be used to computeheadposes in real time. The headpose tracking component may represent aheadpose of a user in a coordinate frame with six degrees of freedomincluding, for example, translation in three perpendicular axes (e.g.,forward/backward, up/down, left/right) and rotation about the threeperpendicular axes (e.g., pitch, yaw, and roll). In some embodiments,sensors 522 may include inertial measurement units that may be used tocompute and/or determine a headpose 514. A headpose 514 for a depth mapmay indicate a present viewpoint of a sensor capturing the depth mapwith six degrees of freedom, for example, but the headpose 514 may beused for other purposes, such as to relate image information to aparticular portion of the physical world or to relate the position ofthe display worn on the user's head to the physical world.

In some embodiments, the headpose information may be derived in otherways than from an IMU, such as from analyzing objects in an image. Forexample, the headpose tracking component may compute relative positionand orientation of an AR device to physical objects based on visualinformation captured by cameras and inertial information captured byIMUs. The headpose tracking component may then compute a headpose of theAR device by, for example, comparing the computed relative position andorientation of the AR device to the physical objects with features ofthe physical objects. In some embodiments, that comparison may be madeby identifying features in images captured with one or more of thesensors 522 that are stable over time such that changes of the positionof these features in images captured over time can be associated with achange in headpose of the user.

The inventors have realized and appreciated techniques for operating XRsystems to provide XR scenes for a more immersive user experience suchas estimating headpose at a frequency of 1 kHz, with low usage ofcomputational resources in connection with an XR device, that may beconfigured with, for example, four video graphic array (VGA) camerasoperating at 30 Hz, one inertial measurement unit (IMU) operating at 1kHz, compute power of a single advanced RISC machine (ARM) core, memoryless than 1 GB, and network bandwidth less than 100 Mbp. Thesetechniques relate to reducing processing required to generate andmaintain maps and estimate headpose as well as to providing andconsuming data with low computational overhead. The XR system maycalculate its pose based on the matched visual features. U.S. patentapplication Ser. No. 16/221,065 describes hybrid tracking and is herebyincorporated herein by reference in its entirety.

In some embodiments, the AR device may construct a map from the featurepoints recognized in successive images in a series of image framescaptured as a user moves throughout the physical world with the ARdevice. Though each image frame may be taken from a different pose asthe user moves, the system may adjust the orientation of the features ofeach successive image frame to match the orientation of the initialimage frame by matching features of the successive image frames topreviously captured image frames. Translations of the successive imageframes so that points representing the same features will matchcorresponding feature points from previously collected image frames, canbe used to align each successive image frame to match the orientation ofpreviously processed image frames. The frames in the resulting map mayhave a common orientation established when the first image frame wasadded to the map. This map, with sets of feature points in a commonframe of reference, may be used to determine the user's pose within thephysical world by matching features from current image frames to themap. In some embodiments, this map may be called a tracking map.

In addition to enabling tracking of the user's pose within theenvironment, this map may enable other components of the system, such asworld reconstruction component 516, to determine the location ofphysical objects with respect to the user. The world reconstructioncomponent 516 may receive the depth maps 512 and headposes 514, and anyother data from the sensors, and integrate that data into areconstruction 518. The reconstruction 518 may be more complete and lessnoisy than the sensor data. The world reconstruction component 516 mayupdate the reconstruction 518 using spatial and temporal averaging ofthe sensor data from multiple viewpoints over time.

The reconstruction 518 may include representations of the physical worldin one or more data formats including, for example, voxels, meshes,planes, etc. The different formats may represent alternativerepresentations of the same portions of the physical world or mayrepresent different portions of the physical world. In the illustratedexample, on the left side of the reconstruction 518, portions of thephysical world are presented as a global surface; on the right side ofthe reconstruction 518, portions of the physical world are presented asmeshes.

In some embodiments, the map maintained by headpose component 514 may besparse relative to other maps that might be maintained of the physicalworld. Rather than providing information about locations, and possiblyother characteristics, of surfaces, the sparse map may indicatelocations of interest points and/or structures, such as corners oredges. In some embodiments, the map may include image frames as capturedby the sensors 522. These frames may be reduced to features, which mayrepresent the interest points and/or structures. In conjunction witheach frame, information about a pose of a user from which the frame wasacquired may also be stored as part of the map. In some embodiments,every image acquired by the sensor may or may not be stored. In someembodiments, the system may process images as they are collected bysensors and select subsets of the image frames for further computation.The selection may be based on one or more criteria that limits theaddition of information yet ensures that the map contains usefulinformation. The system may add a new image frame to the map, forexample, based on overlap with a prior image frame already added to themap or based on the image frame containing a sufficient number offeatures determined as likely to represent stationary objects. In someembodiments, the selected image frames, or groups of features fromselected image frames may serve as key frames for the map, which areused to provide spatial information.

In some embodiments, the amount of data that is processed whenconstructing maps may be reduced, such as by constructing sparse mapswith a collection of mapped points and keyframes and/or dividing themaps into blocks to enable updates by blocks. A mapped point may beassociated with a point of interest in the environment. A keyframe mayinclude selected information from camera-captured data. U.S. patentapplication Ser. No. 16/520,582 describes determining and/or evaluatinglocalization maps and is hereby incorporated herein by reference in itsentirety.

The AR system 502 may integrate sensor data over time from multipleviewpoints of a physical world. The poses of the sensors (e.g., positionand orientation) may be tracked as a device including the sensors ismoved. As the sensor's frame pose is known and how it relates to theother poses, each of these multiple viewpoints of the physical world maybe fused together into a single, combined reconstruction of the physicalworld, which may serve as an abstract layer for the map and providespatial information. The reconstruction may be more complete and lessnoisy than the original sensor data by using spatial and temporalaveraging (i.e., averaging data from multiple viewpoints over time), orany other suitable method.

In the illustrated embodiment in FIG. 3, a map represents the portion ofthe physical world in which a user of a single, wearable device ispresent. In that scenario, headpose associated with frames in the mapmay be represented as a local headpose, indicating orientation relativeto an initial orientation for a single device at the start of a session.For example, the headpose may be tracked relative to an initial headposewhen the device was turned on or otherwise operated to scan anenvironment to build a representation of that environment.

In combination with content characterizing that portion of the physicalworld, the map may include metadata. The metadata, for example, mayindicate time of capture of the sensor information used to form the map.Metadata alternatively or additionally may indicate location of thesensors at the time of capture of information used to form the map.Location may be expressed directly, such as with information from a GPSchip, or indirectly, such as with a wireless (e.g., Wi-Fi) signatureindicating strength of signals received from one or more wireless accesspoints while the sensor data was being collected and/or withidentifiers, such as BSSID's, of wireless access points to which theuser device connected while the sensor data was collected.

The reconstruction 518 may be used for AR functions, such as producing asurface representation of the physical world for occlusion processing orphysics-based processing. This surface representation may change as theuser moves or objects in the physical world change. Aspects of thereconstruction 518 may be used, for example, by a component 520 thatproduces a changing global surface representation in world coordinates,which may be used by other components.

The AR content may be generated based on this information, such as by ARapplications 504. An AR application 504 may be a game program, forexample, that performs one or more functions based on information aboutthe physical world, such as visual occlusion, physics-basedinteractions, and environment reasoning. It may perform these functionsby querying data in different formats from the reconstruction 518produced by the world reconstruction component 516. In some embodiments,component 520 may be configured to output updates when a representationin a region of interest of the physical world changes. That region ofinterest, for example, may be set to approximate a portion of thephysical world in the vicinity of the user of the system, such as theportion within the view field of the user, or is projected(predicted/determined) to come within the view field of the user.

The AR applications 504 may use this information to generate and updatethe AR contents. The virtual portion of the AR contents may be presentedon the display 508 in combination with the see-through reality 510,creating a realistic user experience.

In some embodiments, an AR experience may be provided to a user throughan XR device, which may be a wearable display device, which may be partof a system that may include remote processing and or remote datastorage and/or, in some embodiments, other wearable display devices wornby other users. FIG. 4 illustrates an example of system 580 (hereinafterreferred to as “system 580”) including a single wearable device forsimplicity of illustration. The system 580 includes a head mounteddisplay device 562 (hereinafter referred to as “display device 562”),and various mechanical and electronic modules and systems to support thefunctioning of the display device 562. The display device 562 may becoupled to a frame 564, which is wearable by a display system user orviewer 560 (hereinafter referred to as “user 560”) and configured toposition the display device 562 in front of the eyes of the user 560.According to various embodiments, the display device 562 may be asequential display. The display device 562 may be monocular orbinocular. In some embodiments, the display device 562 may be an exampleof the display 508 in FIG. 3.

In some embodiments, a speaker 566 is coupled to the frame 564 andpositioned proximate an ear canal of the user 560. In some embodiments,another speaker, not shown, is positioned adjacent another ear canal ofthe user 560 to provide for stereo/shapeable sound control. The displaydevice 562 is operatively coupled, such as by a wired lead or wirelessconnectivity 568, to a local data processing module 570 which may bemounted in a variety of configurations, such as fixedly attached to theframe 564, fixedly attached to a helmet or hat worn by the user 560,embedded in headphones, or otherwise removably attached to the user 560(e.g., in a backpack-style configuration, in a belt-coupling styleconfiguration).

The local data processing module 570 may include a processor, as well asdigital memory, such as non-volatile memory (e.g., flash memory), bothof which may be utilized to assist in the processing, caching, andstorage of data. The data include data a) captured from sensors (whichmay be, e.g., operatively coupled to the frame 564) or otherwiseattached to the user 560, such as image capture devices (such ascameras), microphones, inertial measurement units, accelerometers,compasses, GPS units, radio devices, and/or gyros; and/or b) acquiredand/or processed using remote processing module 572 and/or remote datarepository 574, possibly for passage to the display device 562 aftersuch processing or retrieval.

In some embodiments, the wearable deice may communicate with remotecomponents. The local data processing module 570 may be operativelycoupled by communication links 576, 578, such as via a wired or wirelesscommunication links, to the remote processing module 572 and remote datarepository 574, respectively, such that these remote modules 572, 574are operatively coupled to each other and available as resources to thelocal data processing module 570. In further embodiments, in addition oras alternative to remote data repository 574, the wearable device canaccess cloud based remote data repositories, and/or services. In someembodiments, the headpose tracking component described above may be atleast partially implemented in the local data processing module 570. Insome embodiments, the world reconstruction component 516 in FIG. 3 maybe at least partially implemented in the local data processing module570. For example, the local data processing module 570 may be configuredto execute computer executable instructions to generate the map and/orthe physical world representations based at least in part on at least aportion of the data.

In some embodiments, processing may be distributed across local andremote processors. For example, local processing may be used toconstruct a map on a user device (e.g., tracking map) based on sensordata collected with sensors on that user's device. Such a map may beused by applications on that user's device. Additionally, previouslycreated maps (e.g., canonical maps) may be stored in remote datarepository 574. Where a suitable stored or persistent map is available,it may be used instead of or in addition to the tracking map createdlocally on the device. In some embodiments, a tracking map may belocalized to the stored map, such that a correspondence is establishedbetween a tracking map, which might be oriented relative to a positionof the wearable device at the time a user turned the system on, and thecanonical map, which may be oriented relative to one or more persistentfeatures. In some embodiments, the persistent map might be loaded on theuser device to allow the user device to render virtual content without adelay associated with scanning a location to build a tracking map of theuser's full environment from sensor data acquired during the scan. Insome embodiments, the user device may access a remote persistent map(e.g., stored on a cloud) without the need to download the persistentmap on the user device.

In some embodiments, spatial information may be communicated from thewearable device to remote services, such as a cloud service that isconfigured to localize a device to stored maps maintained on the cloudservice. According to one embodiment, the localization processing cantake place in the cloud matching the device location to existing maps,such as canonical maps, and return transforms that link virtual contentto the wearable device location. In such embodiments, the system canavoid communicating maps from remote resources to the wearable device.Other embodiments can be configured for both device-based andcloud-based localization, for example, to enable functionality wherenetwork connectivity is not available or a user opts not to enablecould-based localization.

Alternatively or additionally, the tracking map may be merged orstitched with previously stored maps to extend or improve the quality ofthose maps. The processing to determine whether a suitable previouslycreated environment map is available and/or to merge or stitch atracking map with one or more stored environment maps may be done inlocal data processing module 570 or remote processing module 572.

In some embodiments, the local data processing module 570 may includeone or more processors (e.g., a graphics processing unit (GPU))configured to analyze and process data and/or image information. In someembodiments, the local data processing module 570 may include a singleprocessor (e.g., a single-core or multi-core ARM processor), which wouldlimit the local data processing module 570's compute budget but enable amore miniature device. In some embodiments, the world reconstructioncomponent 516 may use a compute budget less than a single Advanced RISCMachine (ARM) core to generate physical world representations inreal-time on a non-predefined space such that the remaining computebudget of the single ARM core can be accessed for other uses such as,for example, extracting meshes.

In some embodiments, the remote data repository 574 may include adigital data storage facility, which may be available through theInternet or other networking configuration in a “cloud” resourceconfiguration. In some embodiments, all data is stored and allcomputations are performed in the local data processing module 570,allowing fully autonomous use from a remote module. In some embodiments,all data is stored and all or most computations are performed in theremote data repository 574, allowing for a smaller device. A worldreconstruction, for example, may be stored in whole or in part in thisrepository 574.

In embodiments in which data is stored remotely, and accessible over anetwork, data may be shared by multiple users of an augmented realitysystem. For example, user devices may upload their tracking maps toaugment a database of environment maps. In some embodiments, thetracking map upload occurs at the end of a user session with a wearabledevice. In some embodiments, the tracking map uploads may occurcontinuously, semi-continuously, intermittently, at a pre-defined time,after a pre-defined period from the previous upload, or when triggeredby an event. A tracking map uploaded by any user device may be used toexpand or improve a previously stored map, whether based on data fromthat user device or any other user device. Likewise, a persistent mapdownloaded to a user device may be based on data from that user deviceor any other user device. In this way, high quality environment maps maybe readily available to users to improve their experiences with the ARsystem.

In further embodiments, persistent map downloads can be limited and/oravoided based on localization executed on remote resources (e.g., in thecloud). In such configurations, a wearable device or other XR devicecommunicates to the cloud service feature information coupled with poseinformation (e.g., positioning information for the device at the timethe features represented in the feature information were sensed). One ormore components of the cloud service may match the feature informationto respective stored maps (e.g., canonical maps) and generatestransforms between a tracking map maintained by the XR device and thecoordinate system of the canonical map. Each XR device that has itstracking map localized with respect to the canonical map may accuratelyrender virtual content in locations specified with respect to thecanonical map based on its own tracking.

In some embodiments, the local data processing module 570 is operativelycoupled to a battery 582. In some embodiments, the battery 582 is aremovable power source, such as over the counter batteries. In otherembodiments, the battery 582 is a lithium-ion battery. In someembodiments, the battery 582 includes both an internal lithium-ionbattery chargeable by the user 560 during non-operation times of thesystem 580 and removable batteries such that the user 560 may operatethe system 580 for longer periods of time without having to be tetheredto a power source to charge the lithium-ion battery or having to shutthe system 580 off to replace batteries.

FIG. 5A illustrates a user 530 wearing an AR display system rendering ARcontent as the user 530 moves through a physical world environment 532(hereinafter referred to as “environment 532”). The information capturedby the AR system along the movement path of the user may be processedinto one or more tracking maps. The user 530 positions the AR displaysystem at positions 534, and the AR display system records ambientinformation of a passable world (e.g., a digital representation of thereal objects in the physical world that can be stored and updated withchanges to the real objects in the physical world) relative to thepositions 534. That information may be stored as poses in combinationwith images, features, directional audio inputs, or other desired data.The positions 534 are aggregated to data inputs 536, for example, aspart of a tracking map, and processed at least by a passable worldmodule 538, which may be implemented, for example, by processing on aremote processing module 572 of FIG. 4. In some embodiments, thepassable world module 538 may include the headpose component 514 and theworld reconstruction component 516, such that the processed informationmay indicate the location of objects in the physical world incombination with other information about physical objects used inrendering virtual content.

The passable world module 538 determines, at least in part, where andhow AR content 540 can be placed in the physical world as determinedfrom the data inputs 536. The AR content is “placed” in the physicalworld by presenting via the user interface both a representation of thephysical world and the AR content, with the AR content rendered as if itwere interacting with objects in the physical world and the objects inthe physical world presented as if the AR content were, whenappropriate, obscuring the user's view of those objects. In someembodiments, the AR content may be placed by appropriately selectingportions of a fixed element 542 (e.g., a table) from a reconstruction(e.g., the reconstruction 518) to determine the shape and position ofthe AR content 540. As an example, the fixed element may be a table andthe virtual content may be positioned such that it appears to be on thattable. In some embodiments, the AR content may be placed withinstructures in a field of view 544, which may be a present field of viewor an estimated future field of view. In some embodiments, the ARcontent may be persisted relative to a model 546 of the physical world(e.g., a mesh).

As depicted, the fixed element 542 serves as a proxy (e.g., digitalcopy) for any fixed element within the physical world which may bestored in the passable world module 538 so that the user 530 canperceive content on the fixed element 542 without the system having tomap to the fixed element 542 each time the user 530 sees it. The fixedelement 542 may, therefore, be a mesh model from a previous modelingsession or determined from a separate user but nonetheless stored by thepassable world module 538 for future reference by a plurality of users.Therefore, the passable world module 538 may recognize the environment532 from a previously mapped environment and display AR content withouta device of the user 530 mapping all or part of the environment 532first, saving computation process and cycles and avoiding latency of anyrendered AR content.

The mesh model 546 of the physical world may be created by the ARdisplay system and appropriate surfaces and metrics for interacting anddisplaying the AR content 540 can be stored by the passable world module538 for future retrieval by the user 530 or other users without the needto completely or partially recreate the model. In some embodiments, thedata inputs 536 are inputs such as geolocation, user identification, andcurrent activity to indicate to the passable world module 538 whichfixed element 542 of one or more fixed elements are available, which ARcontent 540 has last been placed on the fixed element 542, and whetherto display that same content (such AR content being “persistent” contentregardless of user viewing a particular passable world model).

Even in embodiments in which objects are considered to be fixed (e.g., akitchen table), the passable world module 538 may update those objectsin a model of the physical world from time to time to account for thepossibility of changes in the physical world. The model of fixed objectsmay be updated with a very low frequency. Other objects in the physicalworld may be moving or otherwise not regarded as fixed (e.g., kitchenchairs). To render an AR scene with a realistic feel, the AR system mayupdate the position of these non-fixed objects with a much higherfrequency than is used to update fixed objects. To enable accuratetracking of all of the objects in the physical world, an AR system maydraw information from multiple sensors, including one or more imagesensors.

FIG. 5B is a schematic illustration of a viewing optics assembly 548 andattendant components. In some embodiments, two eye tracking cameras 550,directed toward user eyes 549, detect metrics of the user eyes 549, suchas eye shape, eyelid occlusion, pupil direction and glint on the usereyes 549.

In some embodiments, one of the sensors may be a depth sensor 551, suchas a time-of-flight sensor, emitting signals to the world and detectingreflections of those signals from nearby objects to determine distanceto given objects. A depth sensor, for example, may quickly determinewhether objects have entered the field of view of the user, either as aresult of motion of those objects or a change of pose of the user.However, information about the position of objects in the field of viewof the user may alternatively or additionally be collected with othersensors. Depth information, for example, may be obtained fromstereoscopic visual image sensors or plenoptic sensors.

In some embodiments, world cameras 552 record a greater-than-peripheralview to map and/or otherwise create a model of the environment 532 anddetect inputs that may affect AR content. In some embodiments, the worldcamera 552 and/or camera 553 may be grayscale and/or color imagesensors, which may output grayscale and/or color image frames at fixedtime intervals. Camera 553 may further capture physical world imageswithin a field of view of the user at a specific time. Pixels of aframe-based image sensor may be sampled repetitively even if theirvalues are unchanged. Each of the world cameras 552, the camera 553 andthe depth sensor 551 have respective fields of view of 554, 555, and 556to collect data from and record a physical world scene, such as thephysical world environment 532 depicted in FIG. 34A.

Inertial measurement units 557 may determine movement and orientation ofthe viewing optics assembly 548. In some embodiments, inertialmeasurement units 557 may provide an output indicating a direction ofgravity. In some embodiments, each component is operatively coupled toat least one other component. For example, the depth sensor 551 isoperatively coupled to the eye tracking cameras 550 as a confirmation ofmeasured accommodation against actual distance the user eyes 549 arelooking at.

It should be appreciated that a viewing optics assembly 548 may includesome of the components illustrated in FIG. 34B and may includecomponents instead of or in addition to the components illustrated. Insome embodiments, for example, a viewing optics assembly 548 may includetwo world camera 552 instead of four. Alternatively or additionally,cameras 552 and 553 need not capture a visible light image of their fullfield of view. A viewing optics assembly 548 may include other types ofcomponents. In some embodiments, a viewing optics assembly 548 mayinclude one or more dynamic vision sensor (DVS), whose pixels mayrespond asynchronously to relative changes in light intensity exceedinga threshold.

In some embodiments, a viewing optics assembly 548 may not include thedepth sensor 551 based on time-of-flight information. In someembodiments, for example, a viewing optics assembly 548 may include oneor more plenoptic cameras, whose pixels may capture light intensity andan angle of the incoming light, from which depth information can bedetermined. For example, a plenoptic camera may include an image sensoroverlaid with a transmissive diffraction mask (TDM). Alternatively oradditionally, a plenoptic camera may include an image sensor containingangle-sensitive pixels and/or phase-detection auto-focus pixels (PDAF)and/or micro-lens array (MLA). Such a sensor may serve as a source ofdepth information instead of or in addition to depth sensor 551.

It also should be appreciated that the configuration of the componentsin FIG. 5B is provided as an example. A viewing optics assembly 548 mayinclude components with any suitable configuration, which may be set toprovide the user with the largest field of view practical for aparticular set of components. For example, if a viewing optics assembly548 has one world camera 552, the world camera may be placed in a centerregion of the viewing optics assembly instead of at a side.

Information from the sensors in viewing optics assembly 548 may becoupled to one or more of processors in the system. The processors maygenerate data that may be rendered so as to cause the user to perceivevirtual content interacting with objects in the physical world. Thatrendering may be implemented in any suitable way, including generatingimage data that depicts both physical and virtual objects. In otherembodiments, physical and virtual content may be depicted in one sceneby modulating the opacity of a display device that a user looks throughat the physical world. The opacity may be controlled so as to create theappearance of the virtual object and also to block the user from seeingobjects in the physical world that are occluded by the virtual objects.In some embodiments, the image data may only include virtual contentthat may be modified such that the virtual content is perceived by auser as realistically interacting with the physical world (e.g., clipcontent to account for occlusions), when viewed through the userinterface.

The location on the viewing optics assembly 548 at which content isdisplayed to create the impression of an object at a particular locationmay depend on the physics of the viewing optics assembly. Additionally,the pose of the user's head with respect to the physical world and thedirection in which the user's eyes are looking may impact where in thephysical world content displayed at a particular location on the viewingoptics assembly content will appear. Sensors as described above maycollect this information, and or supply information from which thisinformation may be calculated, such that a processor receiving sensorinputs may compute where objects should be rendered on the viewingoptics assembly 548 to create a desired appearance for the user.

Regardless of how content is presented to a user, a model of thephysical world may be used so that characteristics of the virtualobjects, which can be impacted by physical objects, including the shape,position, motion, and visibility of the virtual object, can be correctlycomputed. In some embodiments, the model may include the reconstructionof a physical world, for example, the reconstruction 518.

That model may be created from data collected from sensors on a wearabledevice of the user. Though, in some embodiments, the model may becreated from data collected by multiple users, which may be aggregatedin a computing device remote from all of the users (and which may be “inthe cloud”).

The model may be created, at least in part, by a world reconstructionsystem such as, for example, the world reconstruction component 516 ofFIG. 3 depicted in more detail in FIG. 6A. The world reconstructioncomponent 516 may include a perception module 660 that may generate,update, and store representations for a portion of the physical world.In some embodiments, the perception module 660 may represent the portionof the physical world within a reconstruction range of the sensors asmultiple voxels. Each voxel may correspond to a 3D cube of apredetermined volume in the physical world, and include surfaceinformation, indicating whether there is a surface in the volumerepresented by the voxel. Voxels may be assigned values indicatingwhether their corresponding volumes have been determined to includesurfaces of physical objects, determined to be empty or have not yetbeen measured with a sensor and so their value is unknown. It should beappreciated that values indicating that voxels that are determined to beempty or unknown need not be explicitly stored, as the values of voxelsmay be stored in computer memory in any suitable way, including storingno information for voxels that are determined to be empty or unknown.

In addition to generating information for a persisted worldrepresentation, the perception module 660 may identify and outputindications of changes in a region around a user of an AR system.Indications of such changes may trigger updates to volumetric datastored as part of the persisted world, or trigger other functions, suchas triggering components 604 that generate AR content to update the ARcontent.

In some embodiments, the perception module 660 may identify changesbased on a signed distance function (SDF) model. The perception module660 may be configured to receive sensor data such as, for example, depthmaps 660 a and headposes 660 b, and then fuse the sensor data into a SDFmodel 660 c. Depth maps 660 a may provide SDF information directly, andimages may be processed to arrive at SDF information. The SDFinformation represents distance from the sensors used to capture thatinformation. As those sensors may be part of a wearable unit, the SDFinformation may represent the physical world from the perspective of thewearable unit and therefore the perspective of the user. The headposes660 b may enable the SDF information to be related to a voxel in thephysical world.

In some embodiments, the perception module 660 may generate, update, andstore representations for the portion of the physical world that iswithin a perception range. The perception range may be determined based,at least in part, on a sensor's reconstruction range, which may bedetermined based, at least in part, on the limits of a sensor'sobservation range. As a specific example, an active depth sensor thatoperates using active IR pulses may operate reliably over a range ofdistances, creating the observation range of the sensor, which may befrom a few centimeters or tens of centimeters to a few meters.

The world reconstruction component 516 may include additional modulesthat may interact with the perception module 660. In some embodiments, apersisted world module 662 may receive representations for the physicalworld based on data acquired by the perception module 660. The persistedworld module 662 also may include various formats of representations ofthe physical world. For example, volumetric information 662 b such asvoxels may be stored as well as meshes 662 c and planes 662 d.Volumetric metadata 662 a may include a size of the volumetricinformation. In some embodiments, other information, such as depth mapscould be saved.

In some embodiments, representations of the physical world, such asthose illustrated in FIG. 6A may provide relatively dense informationabout the physical world in comparison to sparse maps, such as atracking map based on feature points as described above.

In some embodiments, the perception module 660 may include modules thatgenerate representations for the physical world in various formatsincluding, for example, meshes 660 d, planes and semantics 660 e. Therepresentations for the physical world may be stored across local andremote storage mediums. The representations for the physical world maybe described in different coordinate frames depending on, for example,the location of the storage medium. For example, a representation forthe physical world stored in the device may be described in a coordinateframe local to the device. The representation for the physical world mayhave a counterpart stored in a cloud. The counterpart in the cloud maybe described in a coordinate frame shared by all devices in an XRsystem.

In some embodiments, these modules may generate representations based ondata within the perception range of one or more sensors at the time therepresentation is generated as well as data captured at prior times andinformation in the persisted world module 662. In some embodiments,these components may operate on depth information captured with a depthsensor. However, the AR system may include vision sensors and maygenerate such representations by analyzing monocular or binocular visioninformation.

In some embodiments, these modules may operate on regions of thephysical world. Those modules may be triggered to update a subregion ofthe physical world, when the perception module 660 detects a change inthe physical world in that subregion. Such a change, for example, may bedetected by detecting a new surface in the SDF model 660 c or othercriteria, such as changing the value of a sufficient number of voxelsrepresenting the subregion.

The world reconstruction component 516 may include components 664 thatmay receive representations of the physical world from the perceptionmodule 660. Information about the physical world may be pulled by thesecomponents according to, for example, a use request from an application.In some embodiments, information may be pushed to the use components,such as via an indication of a change in a pre-identified region or achange of the physical world representation within the perception range.The components 664, may include, for example, game programs and othercomponents that perform processing for visual occlusion, physics-basedinteractions, and environment reasoning.

Responding to the queries from the components 664, the perception module660 may send representations for the physical world in one or moreformats. For example, when the component 664 indicates that the use isfor visual occlusion or physics-based interactions, the perceptionmodule 660 may send a representation of surfaces. When the component 664indicates that the use is for environmental reasoning, the perceptionmodule 660 may send meshes, planes and semantics of the physical world.

In some embodiments, the perception module 660 may include componentsthat format information to provide the component 664. An example of sucha component may be raycasting component 660 f. A use component (e.g.,component 664), for example, may query for information about thephysical world from a particular point of view. Raycasting component 660f may select from one or more representations of the physical world datawithin a field of view from that point of view.

As should be appreciated from the foregoing description, the perceptionmodule 660, or another component of an AR system, may process data tocreate 3D representations of portions of the physical world. Data to beprocessed may be reduced by culling parts of a 3D reconstruction volumebased at last in part on a camera frustum and/or depth image, extractingand persisting plane data, capturing, persisting, and updating 3Dreconstruction data in blocks that allow local update while maintainingneighbor consistency, providing occlusion data to applicationsgenerating such scenes, where the occlusion data is derived from acombination of one or more depth data sources, and/or performing amulti-stage mesh simplification. The reconstruction may contain data ofdifferent levels of sophistication including, for example, raw data suchas live depth data, fused volumetric data such as voxels, and computeddata such as meshes.

In some embodiments, components of a passable world model may bedistributed, with some portions executing locally on an XR device andsome portions executing remotely, such as on a network connected server,or otherwise in the cloud. The allocation of the processing and storageof information between the local XR device and the cloud may impactfunctionality and user experience of an XR system. For example, reducingprocessing on a local device by allocating processing to the cloud mayenable longer battery life and reduce heat generated on the localdevice. But, allocating too much processing to the cloud may createundesirable latency that causes an unacceptable user experience.

FIG. 6B depicts a distributed component architecture 600 configured forspatial computing, according to some embodiments. The distributedcomponent architecture 600 may include a passable world component 602(e.g., PW 538 in FIG. 5A), a Lumin OS 604, API's 606, SDK 608, andApplication 610. The Lumin OS 604 may include a Linux-based kernel withcustom drivers compatible with an XR device. The API's 606 may includeapplication programming interfaces that grant XR applications (e.g.,Applications 610) access to the spatial computing features of an XRdevice. The SDK 608 may include a software development kit that allowsthe creation of XR applications.

One or more components in the architecture 600 may create and maintain amodel of a passable world. In this example sensor data is collected on alocal device. Processing of that sensor data may be performed in partlocally on the XR device and partially in the cloud. PW 538 may includeenvironment maps created based, at least in part, on data captured by ARdevices worn by multiple users. During sessions of an AR experience,individual AR devices (such as wearable devices described above inconnection with FIG. 4 may create tracking maps, which is one type ofmap.

In some embodiments, the device may include components that constructboth sparse maps and dense maps. A tracking map may serve as a sparsemap and may include headposes of the AR device scanning an environmentas well as information about objects detected within that environment ateach headpose. Those headposes may be maintained locally for eachdevice. For example, the headpose on each device may be relative to aninitial headpose when the device was turned on for its session. As aresult, each tracking map may be local to the device creating it and mayhave its own frame of reference defined by its own local coordinatesystem. In some embodiments, however, the tracking map on each devicemay be formed such that one coordinate of its local coordinate system isaligned with the direction of gravity as measured by its sensors, suchas inertial measurement unit 557.

The dense map may include surface information, which may be representedby a mesh or depth information. Alternatively or additionally, a densemap may include higher level information derived from surface or depthinformation, such as the location and/or characteristics of planesand/or other objects.

Creation of the dense maps may be independent of the creation of sparsemaps, in some embodiments. The creation of dense maps and sparse maps,for example, may be performed in separate processing pipelines within anAR system. Separating processing, for example, may enable generation orprocessing of different types of maps to be performed at differentrates. Sparse maps, for example, may be refreshed at a faster rate thandense maps. In some embodiments, however, the processing of dense andsparse maps may be related, even if performed in different pipelines.Changes in the physical world revealed in a sparse map, for example, maytrigger updates of a dense map, or vice versa. Further, even ifindependently created, the maps might be used together. For example, acoordinate system derived from a sparse map may be used to defineposition and/or orientation of objects in a dense map.

The sparse map and/or dense map may be persisted for re-use by the samedevice and/or sharing with other devices. Such persistence may beachieved by storing information in the cloud. The AR device may send thetracking map to a cloud to, for example, merge or stitch withenvironment maps selected from persisted maps previously stored in thecloud. In some embodiments, the selected persisted maps may be sent fromthe cloud to the AR device for merging or stitching. In someembodiments, the persisted maps may be oriented with respect to one ormore persistent coordinate frames. Such maps may serve as canonicalmaps, as they can be used by any of multiple devices. In someembodiments, a model of a passable world may comprise or be created fromone or more canonical maps. Devices, even though they perform someoperations based on a coordinate frame local to the device, maynonetheless use the canonical map by determining a transformationbetween their coordinate frame local to the device and the canonicalmap.

A canonical map may originate as a tracking map (TM) (e.g., TM 1102 inFIG. 31A), which may be promoted to a canonical map. The canonical mapmay be persisted such that devices that access the canonical map may,once determining a transformation between their local coordinate systemand a coordinate system of the canonical map, use the information in thecanonical map to determine locations of objects represented in thecanonical map in the physical world around the device. In someembodiments, a TM may be a headpose sparse map created by an XR device.In some embodiments, the canonical map may be created when an XR devicesends one or more TMs to a cloud server for merging or stitching withadditional TMs captured by the XR device at a different time or by otherXR devices.

In embodiments in which tracking maps are formed on local devices withone coordinate of a local coordinate frame aligned with gravity, thisorientation with respect to gravity may be preserved upon creation of acanonical map. For example, when a tracking map that is submitted formerging or stitching does not overlap with any previously stored map,that tracking map may be promoted to a canonical map. Other trackingmaps, which may also have an orientation relative to gravity, may besubsequently merged or stitched with that canonical map. The merging orstitching may be done so as to ensure that the resulting canonical mapretains its orientation relative to gravity. Two maps, for example, maynot be merged or stitched, regardless of correspondence of featurepoints in those maps, if coordinates of each map aligned with gravity donot align with each other with a sufficiently close tolerance.

The canonical maps, or other maps, may provide information about theportions of the physical world represented by the data processed tocreate respective maps. FIG. 7 depicts an exemplary tracking map 700,according to some embodiments. The tracking map 700 may provide a floorplan 706 of physical objects in a corresponding physical world,represented by points 702. In some embodiments, a map point 702 mayrepresent a feature of a physical object that may include multiplefeatures. For example, each corner of a table may be a feature that isrepresented by a point on a map. The features may be derived fromprocessing images, such as may be acquired with the sensors of awearable device in an augmented reality system. The features, forexample, may be derived by processing an image frame output by a sensorto identify features based on large gradients in the image or othersuitable criteria. Further processing may limit the number of featuresin each frame. For example, processing may select features that likelyrepresent persistent objects. One or more heuristics may be applied forthis selection.

The tracking map 700 may include data on points 702 collected by adevice. For each image frame with data points included in a trackingmap, a pose may be stored. The pose may represent the orientation fromwhich the image frame was captured, such that the feature points withineach image frame may be spatially correlated. The pose may be determinedby positioning information, such as may be derived from the sensors,such as an IMU sensor, on the wearable device. Alternatively oradditionally, the pose may be determined from matching image frames toother image frames that depict overlapping portions of the physicalworld. By finding such positional correlation, which may be accomplishedby matching subsets of features points in two frames, the relative posebetween the two frames may be computed. A relative pose may be adequatefor a tracking map, as the map may be relative to a coordinate systemlocal to a device established based on the initial pose of the devicewhen construction of the tracking map was initiated.

Not all of the feature points and image frames collected by a device maybe retained as part of the tracking map, as much of the informationcollected with the sensors is likely to be redundant. Rather, onlycertain frames may be added to the map. Those frames may be selectedbased on one or more criteria, such as degree of overlap with imageframes already in the map, the number of new features they contain or aquality metric for the features in the frame. Image frames not added tothe tracking map may be discarded or may be used to revise the locationof features. As a further alternative, all or most of the image frames,represented as a set of features may be retained, but a subset of thoseframes may be designated as key frames, which are used for furtherprocessing.

The key frames may be processed to produce keyrigs 704. The key framesmay be processed to produce three dimensional sets of feature points andsaved as keyrigs 704. Such processing may entail, for example, comparingimage frames derived simultaneously from two cameras to stereoscopicallydetermine the 3D position of feature points. Metadata may be associatedwith these keyframes and/or keyrigs, such as poses.

The environment maps may have any of multiple formats depending on, forexample, the storage locations of an environment map including, forexample, local storage of AR devices and remote storage. For example, amap in remote storage may have higher resolution than a map in localstorage on a wearable device where memory is limited. To send a higherresolution map from remote storage to local storage, the map may be downsampled or otherwise converted to an appropriate format, such as byreducing the number of poses per area of the physical world stored inthe map and/or the number of feature points stored for each pose. Insome embodiments, a slice or portion of a high-resolution map fromremote storage may be sent to local storage, where the slice or portionis not down sampled.

A database of environment maps may be updated as new tracking maps arecreated. To determine which of a potentially very large number ofenvironment maps in a database is to be updated, updating may includeefficiently selecting one or more environment maps stored in thedatabase relevant to the new tracking map. The selected one or moreenvironment maps may be ranked by relevance and one or more of thehighest-ranking maps may be selected for processing to merge or stitchhigher ranked selected environment maps with the new tracking map tocreate one or more updated environment maps. When a new tracking maprepresents a portion of the physical world for which there is nopreexisting environment map to update, that tracking map may be storedin the database as a new environment map.

View Independent Display

Described herein are methods and apparatus for providing virtualcontents using an XR system, independent of locations of eyes viewingthe virtual content. Conventionally, a virtual content is re-renderedupon any motion of the displaying system. For example, if a user wearinga display system views a virtual representation of a three-dimensional(3D) object on the display and walks around the area where the 3D objectappears, the 3D object should be re-rendered for each viewpoint suchthat the user has the perception that he or she is walking around anobject that occupies real space. However, the re-rendering consumessignificant computational resources of a system and causes artifacts dueto latency.

The inventors have recognized and appreciated that headpose (e.g., thelocation and orientation of a user wearing an XR system) may be used torender a virtual content independent of eye rotations within a head ofthe user. In some embodiments, dynamic maps of a scene may be generatedbased on multiple coordinate frames in real space across one or moresessions such that virtual contents interacting with the dynamic mapsmay be rendered robustly, independent of eye rotations within the headof the user and/or independent of sensor deformations caused by, forexample, heat generated during high-speed, computation-intensiveoperation. In some embodiments, the configuration of multiple coordinateframes may enable a first XR device worn by a first user and a second XRdevice worn by a second user to recognize a common location in a scene.In some embodiments, the configuration of multiple coordinate frames mayenable users wearing XR devices to view a virtual content in a samelocation of a scene.

In some embodiments, a tracking map may be built in a world coordinateframe, which may have a world origin. The world origin may be the firstpose of an XR device when the XR device is powered on. The world originmay be aligned to gravity such that a developer of an XR application canget gravity alignment without extra work. Different tracking maps may bebuilt in different world coordinate frames because the tracking maps maybe captured by a same XR device at different sessions and/or differentXR devices worn by different users. In some embodiments, a session of anXR device may span from powering on to powering off the device. In someembodiments, an XR device may have a head coordinate frame, which mayhave a head origin. The head origin may be the current pose of an XRdevice when an image is taken. The difference between headpose of aworld coordinate frame and of a head coordinate frame may be used toestimate a tracking route.

In some embodiments, an XR device may have a camera coordinate frame,which may have a camera origin. The camera origin may be the currentpose of one or more sensors of an XR device. The inventors haverecognized and appreciated that the configuration of a camera coordinateframe enables robust displaying virtual contents independent of eyerotation within a head of a user. This configuration also enables robustdisplaying of virtual contents independent of sensor deformation due to,for example, heat generated during operation.

In some embodiments, an XR device may have a head unit with ahead-mountable frame that a user can secure to their head and mayinclude two waveguides, one in front of each eye of the user. Thewaveguides may be transparent so that ambient light from real-worldobjects can transmit through the waveguides and the user can see thereal-world objects. Each waveguide may transmit projected light from aprojector to a respective eye of the user. The projected light may forman image on the retina of the eye. The retina of the eye thus receivesthe ambient light and the projected light. The user may simultaneouslysee real-world objects and one or more virtual objects that are createdby the projected light. In some embodiments, XR devices may have sensorsthat detect real-world objects around a user. These sensors may, forexample, be cameras that capture images that may be processed toidentify the locations of real-world objects.

In some embodiments, an XR system may assign a coordinate frame to avirtual content, as opposed to attaching the virtual content in a worldcoordinate frame. Such configuration enables a virtual content to bedescribed without regard to where it is rendered for a user, but it maybe attached to a more persistent frame position such as a persistentcoordinate frame (PCF) described in relation to, for example, FIGS.14-20C, to be rendered in a specified location. When the locations ofthe objects change, the XR device may detect the changes in theenvironment map and determine movement of the head unit worn by the userrelative to real-world objects.

FIG. 8 illustrates a user experiencing virtual content, as rendered byan XR system 10, in a physical environment, according to someembodiments. The XR system may include a first XR device 12.1 that isworn by a first user 14.1, a network 18 and a server 20. The user 14.1is in a physical environment with a real object in the form of a table16.

In the illustrated example, the first XR device 12.1 includes a headunit 22, a belt pack 24 and a cable connection 26. The first user 14.1secures the head unit 22 to their head and the belt pack 24 remotelyfrom the head unit 22 on their waist. The cable connection 26 connectsthe head unit 22 to the belt pack 24. The head unit 22 includestechnologies that are used to display a virtual object or objects to thefirst user 14.1 while the first user 14.1 is permitted to see realobjects such as the table 16. The belt pack 24 includes primarilyprocessing and communications capabilities of the first XR device 12.1.In some embodiments, the processing and communication capabilities mayreside entirely or partially in the head unit 22 such that the belt pack24 may be removed or may be located in another device such as abackpack.

In the illustrated example, the belt pack 24 is connected via a wirelessconnection to the network 18. The server 20 is connected to the network18 and holds data representative of local content. The belt pack 24downloads the data representing the local content from the server 20 viathe network 18. The belt pack 24 provides the data via the cableconnection 26 to the head unit 22. The head unit 22 may include adisplay that has a light source, for example, a laser light source or alight emitting diode (LED), and a waveguide that guides the light.

In some embodiments, the first user 14.1 may mount the head unit 22 totheir head and the belt pack 24 to their waist. The belt pack 24 maydownload image data representing virtual content over the network 18from the server 20. The first user 14.1 may see the table 16 through adisplay of the head unit 22. A projector forming part of the head unit22 may receive the image data from the belt pack 24 and generate lightbased on the image data. The light may travel through one or more of thewaveguides forming part of the display of the head unit 22. The lightmay then leave the waveguide and propagates onto a retina of an eye ofthe first user 14.1. The projector may generate the light in a patternthat is replicated on a retina of the eye of the first user 14.1. Thelight that falls on the retina of the eye of the first user 14.1 mayhave a selected field of depth so that the first user 14.1 perceives animage at a preselected depth behind the waveguide. In addition, botheyes of the first user 14.1 may receive slightly different images sothat a brain of the first user 14.1 perceives a three-dimensional imageor images at selected distances from the head unit 22. In theillustrated example, the first user 14.1 perceives a virtual content 28above the table 16. The proportions of the virtual content 28 and itslocation and distance from the first user 14.1 are determined by thedata representing the virtual content 28 and various coordinate framesthat are used to display the virtual content 28 to the first user 14.1.

In the illustrated example, the virtual content 28 is not visible fromthe perspective of the drawing and is visible to the first user 14.1through using the first XR device 12.1. The virtual content 28 mayinitially reside as data structures within vision data and algorithms inthe belt pack 24. The data structures may then manifest themselves aslight when the projectors of the head unit 22 generate light based onthe data structures. It should be appreciated that although the virtualcontent 28 has no existence in three-dimensional space in front of thefirst user 14.1, the virtual content 28 is still represented in FIG. 1in three-dimensional space for illustration of what a wearer of headunit 22 perceives. The visualization of computer data inthree-dimensional space may be used in this description to illustratehow the data structures that facilitate the renderings are perceived byone or more users relate to one another within the data structures inthe belt pack 24.

FIG. 9 illustrates components of the first XR device 12.1, according tosome embodiments. The first XR device 12.1 may include the head unit 22,and various components forming part of the vision data and algorithmsincluding, for example, a rendering engine 30, various coordinatesystems 32, various origin and destination coordinate frames 34, andvarious origin to destination coordinate frame transformers 36. Thevarious coordinate systems may be based on intrinsics of to the XRdevice or may be determined by reference to other information, such as apersistent pose or a persistent coordinate system, as described herein.

The head unit 22 may include a head-mountable frame 40, a display system42, a real object detection camera 44, a movement tracking camera 46,and an inertial measurement unit 48.

The head-mountable frame 40 may have a shape that is securable to thehead of the first user 14.1 in FIG. 8. The display system 42, realobject detection camera 44, movement tracking camera 46, and inertialmeasurement unit 48 may be mounted to the head-mountable frame 40 andtherefore move together with the head-mountable frame 40.

The coordinate systems 32 may include a local data system 52, a worldframe system 54, a head frame system 56, and a camera frame system 58.

The local data system 52 may include a data channel 62, a local framedetermining routine 64 and a local frame storing instruction 66. Thedata channel 62 may be an internal software routine, a hardwarecomponent such as an external cable or a radio frequency receiver, or ahybrid component such as a port that is opened up. The data channel 62may be configured to receive image data 68 representing a virtualcontent.

The local frame determining routine 64 may be connected to the datachannel 62. The local frame determining routine 64 may be configured todetermine a local coordinate frame 70. In some embodiments, the localframe determining routine may determine the local coordinate frame basedon real world objects or real-world locations. In some embodiments, thelocal coordinate frame may be based on a top edge relative to a bottomedge of a browser window, head or feet of a character, a node on anouter surface of a prism or bounding box that encloses the virtualcontent, or any other suitable location to place a coordinate frame thatdefines a facing direction of a virtual content and a location (e.g., anode, such as a placement node or PCF node) with which to place thevirtual content, etc.

The local frame storing instruction 66 may be connected to the localframe determining routine 64. One skilled in the art will understandthat software modules and routines are “connected” to one anotherthrough subroutines, calls, etc. The local frame storing instruction 66may store the local coordinate frame 70 as a local coordinate frame 72within the origin and destination coordinate frames 34. In someembodiments, the origin and destination coordinate frames 34 may be oneor more coordinate frames that may be manipulated or transformed inorder for a virtual content to persist between sessions. In someembodiments, a session may be the period of time between a boot-up andshut-down of an XR device. Two sessions may be two start-up andshut-down periods for a single XR device, or may be a start-up andshut-down for two different XR devices.

In some embodiments, the origin and destination coordinate frames 34 maybe the coordinate frames involved in one or more transformationsrequired in order for a first user's XR device and a second user's XRdevice to recognize a common location. In some embodiments, thedestination coordinate frame may be the output of a series ofcomputations and transformations applied to the target coordinate framein order for a first and second user to view a virtual content in thesame location.

The rendering engine 30 may be connected to the data channel 62. Therendering engine 30 may receive the image data 68 from the data channel62 such that the rendering engine 30 may render virtual content based,at least in part, on the image data 68.

The display system 42 may be connected to the rendering engine 30. Thedisplay system 42 may include components that transform the image data68 into visible light. The visible light may form two patterns, one foreach eye. The visible light may enter eyes of the first user 14.1 inFIG. 8 and may be detected on retinas of the eyes of the first user14.1.

The real object detection camera 44 may include one or more cameras thatmay capture images from different sides of the head-mountable frame 40.The movement tracking camera 46 may include one or more cameras thatcapture images on sides of the head-mountable frame 40. One set of oneor more cameras may be used instead of the two sets of one or morecameras representing the real object detection camera(s) 44 and themovement tracking camera(s) 46. In some embodiments, the cameras 44, 46may capture images. As described above these cameras may collect datathat is used to construct a tacking map.

The inertial measurement unit 48 may include a number of devices thatare used to detect movement of the head unit 22. The inertialmeasurement unit 48 may include a gravitation sensor, one or moreaccelerometers and one or more gyroscopes. The sensors of the inertialmeasurement unit 48, in combination, track movement of the head unit 22in at least three orthogonal directions and about at least threeorthogonal axes.

In the illustrated example, the world frame system 54 includes a worldsurface determining routine 78, a world frame determining routine 80,and a world frame storing instruction 82. The world surface determiningroutine 78 is connected to the real object detection camera 44. Theworld surface determining routine 78 receives images and/or key framesbased on the images that are captured by the real object detectioncamera 44 and processes the images to identify surfaces in the images. Adepth sensor (not shown) may determine distances to the surfaces. Thesurfaces are thus represented by data in three dimensions includingtheir sizes, shapes, and distances from the real object detectioncamera.

In some embodiments, a world coordinate frame 84 may be based on theorigin at the initialization of the headpose session. In someembodiments, the world coordinate frame may be located where the devicewas booted up, or could be somewhere new if headpose was lost during theboot session. In some embodiments, the world coordinate frame may be theorigin at the start of a headpose session.

In the illustrated example, the world frame determining routine 80 isconnected to the world surface determining routine 78 and determines aworld coordinate frame 84 based on the locations of the surfaces asdetermined by the world surface determining routine 78. The world framestoring instruction 82 is connected to the world frame determiningroutine 80 to receive the world coordinate frame 84 from the world framedetermining routine 80. The world frame storing instruction 82 storesthe world coordinate frame 84 as a world coordinate frame 86 within theorigin and destination coordinate frames 34.

The head frame system 56 may include a head frame determining routine 90and a head frame storing instruction 92. The head frame determiningroutine 90 may be connected to the movement tracking camera 46 and theinertial measurement unit 48. The head frame determining routine 90 mayuse data from the movement tracking camera 46 and the inertialmeasurement unit 48 to calculate a head coordinate frame 94. Forexample, the inertial measurement unit 48 may have a gravitation sensorthat determines the direction of gravitational force relative to thehead unit 22. The movement tracking camera 46 may continually captureimages that are used by the head frame determining routine 90 to refinethe head coordinate frame 94. The head unit 22 moves when the first user14.1 in FIG. 8 moves their head. The movement tracking camera 46 and theinertial measurement unit 48 may continuously provide data to the headframe determining routine 90 so that the head frame determining routine90 can update the head coordinate frame 94.

The head frame storing instruction 92 may be connected to the head framedetermining routine 90 to receive the head coordinate frame 94 from thehead frame determining routine 90. The head frame storing instruction 92may store the head coordinate frame 94 as a head coordinate frame 96among the origin and destination coordinate frames 34. The head framestoring instruction 92 may repeatedly store the updated head coordinateframe 94 as the head coordinate frame 96 when the head frame determiningroutine 90 recalculates the head coordinate frame 94. In someembodiments, the head coordinate frame may be the location of thewearable XR device 12.1 relative to the local coordinate frame 72.

The camera frame system 58 may include camera intrinsics 98. The cameraintrinsics 98 may include dimensions of the head unit 22 that arefeatures of its design and manufacture. The camera intrinsics 98 may beused to calculate a camera coordinate frame 100 that is stored withinthe origin and destination coordinate frames 34.

In some embodiments, the camera coordinate frame 100 may include allpupil positions of a left eye of the first user 14.1 in FIG. 8. When theleft eye moves from left to right or up and down, the pupil positions ofthe left eye are located within the camera coordinate frame 100. Inaddition, the pupil positions of a right eye are located within a cameracoordinate frame 100 for the right eye. In some embodiments, the cameracoordinate frame 100 may include the location of the camera relative tothe local coordinate frame when an image is taken.

The origin to destination coordinate frame transformers 36 may include alocal-to-world coordinate transformer 104, a world-to-head coordinatetransformer 106, and a head-to-camera coordinate transformer 108. Thelocal-to-world coordinate transformer 104 may receive the localcoordinate frame 72 and transform the local coordinate frame 72 to theworld coordinate frame 86. The transformation of the local coordinateframe 72 to the world coordinate frame 86 may be represented as a localcoordinate frame transformed to world coordinate frame 110 within theworld coordinate frame 86.

The world-to-head coordinate transformer 106 may transform from theworld coordinate frame 86 to the head coordinate frame 96. Theworld-to-head coordinate transformer 106 may transform the localcoordinate frame transformed to world coordinate frame 110 to the headcoordinate frame 96. The transformation may be represented as a localcoordinate frame transformed to head coordinate frame 112 within thehead coordinate frame 96.

The head-to-camera coordinate transformer 108 may transform from thehead coordinate frame 96 to the camera coordinate frame 100. Thehead-to-camera coordinate transformer 108 may transform the localcoordinate frame transformed to head coordinate frame 112 to a localcoordinate frame transformed to camera coordinate frame 114 within thecamera coordinate frame 100. The local coordinate frame transformed tocamera coordinate frame 114 may be entered into the rendering engine 30.The rendering engine 30 may render the image data 68 representing thelocal content 28 based on the local coordinate frame transformed tocamera coordinate frame 114.

FIG. 10 is a spatial representation of the various origin anddestination coordinate frames 34. The local coordinate frame 72, worldcoordinate frame 86, head coordinate frame 96, and camera coordinateframe 100 are represented in the figure. In some embodiments, the localcoordinate frame associated with the XR content 28 may have a positionand rotation (e.g., may provide a node and facing direction) relative toa local and/or world coordinate frame and/or PCF when the virtualcontent is placed in the real world so the virtual content may be viewedby the user. Each camera may have its own camera coordinate frame 100encompassing all pupil positions of one eye. Reference numerals 104A and106A represent the transformations that are made by the local-to-worldcoordinate transformer 104, world-to-head coordinate transformer 106,and head-to-camera coordinate transformer 108 in FIG. 9, respectively.

FIG. 11 depicts a camera render protocol for transforming from a headcoordinate frame to a camera coordinate frame, according to someembodiments. In the illustrated example, a pupil for a single eye movesfrom position A to B. A virtual object that is meant to appearstationary will project onto a depth plane at one of the two positions Aor B depending on the position of the pupil (assuming that the camera isconfigured to use a pupil-based coordinate frame). As a result, using apupil coordinate frame transformed to a head coordinate frame will causejitter in a stationary virtual object as the eye moves from position Ato position B. This situation is referred to as view dependent displayor projection.

As depicted in FIG. 12, a camera coordinate frame (e.g., CR) ispositioned and encompasses all pupil positions and object projectionwill now be consistent regardless of pupil positions A and B. The headcoordinate frame transforms to the CR frame, which is referred to asview independent display or projection. An image reprojection may beapplied to the virtual content to account for a change in eye position,however, as the rendering is still in the same position, jitter isminimized.

FIG. 13 illustrates the display system 42 in more detail. The displaysystem 42 includes a stereoscopic analyzer 144 that is connected to therendering engine 30 and forms part of the vision data and algorithms.

The display system 42 further includes left and right projectors 166Aand 166B and left and right waveguides 170A and 170B. The left and rightprojectors 166A and 166B are connected to power supplies. Each projector166A and 166B has a respective input for image data to be provided tothe respective projector 166A or 166B. The respective projector 166A or166B, when powered, generates light in two-dimensional patterns andemanates the light therefrom. The left and right waveguides 170A and170B are positioned to receive light from the left and right projectors166A and 166B, respectively. The left and right waveguides 170A and 1706are transparent waveguides.

In use, a user mounts the head mountable frame 40 to their head.Components of the head mountable frame 40 may, for example, include astrap (not shown) that wraps around the back of the head of the user.The left and right waveguides 170A and 170B are then located in front ofleft and right eyes 220A and 220B of the user.

The rendering engine 30 enters the image data that it receives into thestereoscopic analyzer 144. The image data is three-dimensional imagedata of the local content 28 in FIG. 8. The image data is projected ontoa plurality of virtual planes. The stereoscopic analyzer 144 analyzesthe image data to determine left and right image data sets based on theimage data for projection onto each depth plane. The left and rightimage data sets are data sets that represent two-dimensional images thatare projected in three-dimensions to give the user a perception of adepth.

The stereoscopic analyzer 144 enters the left and right image data setsinto the left and right projectors 166A and 166B. The left and rightprojectors 166A and 166B then create left and right light patterns. Thecomponents of the display system 42 are shown in plain view, although itshould be understood that the left and right patterns aretwo-dimensional patterns when shown in front elevation view. Each lightpattern includes a plurality of pixels. For purposes of illustration,light rays 224A and 226A from two of the pixels are shown leaving theleft projector 166A and entering the left waveguide 170A. The light rays224A and 226A reflect from sides of the left waveguide 170A. It is shownthat the light rays 224A and 226A propagate through internal reflectionfrom left to right within the left waveguide 170A, although it should beunderstood that the light rays 224A and 226A also propagate in adirection into the paper using refractory and reflective systems.

The light rays 224A and 226A exit the left light waveguide 170A througha pupil 228A and then enter a left eye 220A through a pupil 230A of theleft eye 220A. The light rays 224A and 226A then fall on a retina 232Aof the left eye 220A. In this manner, the left light pattern falls onthe retina 232A of the left eye 220A. The user is given the perceptionthat the pixels that are formed on the retina 232A are pixels 234A and236A that the user perceives to be at some distance on a side of theleft waveguide 170A opposing the left eye 220A. Depth perception iscreated by manipulating the focal length of the light.

In a similar manner, the stereoscopic analyzer 144 enters the rightimage data set into the right projector 166B. The right projector 166Btransmits the right light pattern, which is represented by pixels in theform of light rays 224B and 226B. The light rays 224B and 226B reflectwithin the right waveguide 170B and exit through a pupil 228B. The lightrays 224B and 226B then enter through a pupil 230B of the right eye 220Band fall on a retina 232B of a right eye 220B. The pixels of the lightrays 224B and 226B are perceived as pixels 134B and 236B behind theright waveguide 170B.

The patterns that are created on the retinas 232A and 232B areindividually perceived as left and right images. The left and rightimages differ slightly from one another due to the functioning of thestereoscopic analyzer 144. The left and right images are perceived in amind of the user as a three-dimensional rendering.

As mentioned, the left and right waveguides 170A and 170B aretransparent. Light from a real-life object such as the table 16 on aside of the left and right waveguides 170A and 170B opposing the eyes220A and 220B can project through the left and right waveguides 170A and170B and fall on the retinas 232A and 232B.

Persistent Coordinate Frame (PCF)

Described herein are methods and apparatus for providing spatialpersistence across user instances within a shared space. Without spatialpersistence, virtual content placed in the physical world by a user in asession may not exist or may be misplaced in the user's view in adifferent session. Without spatial persistence, virtual content placedin the physical world by one user may not exist or may be out of placein a second user's view, even if the second user is intended to besharing an experience of the same physical space with the first user.

The inventors have recognized and appreciated that spatial persistencemay be provided through persistent coordinate frames (PCFs). A PCF maybe defined based on one or more points, representing features recognizedin the physical world (e.g., corners, edges). The features may beselected such that they are likely to be the same from a user instanceto another user instance of an XR system.

Further, drift during tracking, which causes the computed tracking path(e.g., camera trajectory) to deviate from the actual tracking path, cancause the location of virtual content, when rendered with respect to alocal map that is based solely on a tracking map to appear out of place.A tracking map for the space may be refined to correct the drifts as anXR device collects more information of the scene overtime. However, ifvirtual content is placed on a real object before a map refinement andsaved with respect to the world coordinate frame of the device derivedfrom the tracking map, the virtual content may appear displaced, as ifthe real object has been moved during the map refinement. PCFs may beupdated according to map refinement because the PCFs are defined basedon the features and are updated as the features move during maprefinements.

A PCF may comprise six degrees of freedom with translations androtations relative to a map coordinate system. A PCF may be stored in alocal and/or remote storage medium. The translations and rotations of aPCF may be computed relative to a map coordinate system depending on,for example, the storage location. For example, a PCF used locally by adevice may have translations and rotations relative to a worldcoordinate frame of the device. A PCF in the cloud may have translationsand rotations relative to a canonical coordinate frame of a canonicalmap.

PCFs may provide a sparse representation of the physical world,providing less than all of the available information about the physicalworld, such that they may be efficiently processed and transferred.Techniques for processing persistent spatial information may includecreating dynamic maps based on one or more coordinate systems in realspace across one or more sessions, generating persistent coordinateframes (PCF) over the sparse maps, which may be exposed to XRapplications via, for example, an application programming interface(API).

FIG. 14 is a block diagram illustrating the creation of a persistentcoordinate frame (PCF) and the attachment of XR content to the PCF,according to some embodiments. Each block may represent digitalinformation stored in a computer memory. In the case of applications1180, the data may represent computer-executable instructions. In thecase of virtual content 1170, the digital information may define avirtual object, as specified by the application 1180, for example. Inthe case of the other boxes, the digital information may characterizesome aspect of the physical world.

In the illustrated embodiment, one or more PCFs are created from imagescaptured with sensors on a wearable device. In the embodiment of FIG.14, the sensors are visual image cameras. These cameras may be the samecameras used for forming a tracking map. Accordingly, some of theprocessing suggested by FIG. 14 may be performed as part of updating atracking map. However, FIG. 14 illustrates that information thatprovides persistence is generated in addition to the tracking map.

In order to derive a 3D PCF, two images 1110 from two cameras mounted toa wearable device in a configuration that enables stereoscopic imageanalysis are processed together. FIG. 14 illustrates an Image 1 and anImage 2, each derived from one of the cameras. A single image from eachcamera is illustrated for simplicity. However, each camera may output astream of image frames and the processing illustrated in FIG. 14 may beperformed for multiple image frames in the stream.

Accordingly, Image 1 and Image 2 may each be one frame in a sequence ofimage frames. Processing as depicted in FIG. 14 may be repeated onsuccessive image frames in the sequence until image frames containingfeature points providing a suitable image from which to form persistentspatial information is processed. Alternatively or additionally, theprocessing of FIG. 14 might be repeated as a user moves such that theuser is no longer close enough to a previously identified PCF toreliably use that PCF for determining positions with respect to thephysical world. For example, an XR system may maintain a current PCF fora user. When that distance exceeds a threshold, the system may switch toa new current PCF, closer to the user, which may be generated accordingto the process of FIG. 14, using image frames acquired in the user'scurrent location.

Even when generating a single PCF, a stream of image frames may beprocessed to identify image frames depicting content in the physicalworld that is likely stable and can be readily identified by a device inthe vicinity of the region of the physical world depicted in the imageframe. In the embodiment of FIG. 14, this processing begins with theidentification of features 1120 in the image. Features may beidentified, for example, by finding locations of gradients in the imageabove a threshold or other characteristics, which may correspond to acorner of an object, for example. In the embodiment illustrated, thefeatures are points, but other recognizable features, such as edges, mayalternatively or additionally be used.

In the embodiment illustrated, a fixed number, N, of features 1120 areselected for further processing. Those feature points may be selectedbased on one or more criteria, such as magnitude of the gradient, orproximity to other feature points. Alternatively or additionally, thefeature points may be selected heuristically, such as based oncharacteristics that suggest the feature points are persistent. Forexample, heuristics may be defined based on the characteristics offeature points that likely correspond to a corner of a window or a dooror a large piece of furniture. Such heuristics may take into account thefeature point itself and what surrounds it. As a specific example, thenumber of feature points per image may be between 100 and 500 or between150 and 250, such as 200.

Regardless of the number of feature points selected, descriptors 1130may be computed for the feature points. In this example, a descriptor iscomputed for each selected feature point, but a descriptor may becomputed for groups of feature points or for a subset of the featurepoints or for all features within an image. The descriptor characterizesa feature point such that feature points representing the same object inthe physical world are assigned similar descriptors. The descriptors mayfacilitate alignment of two frames, such as may occur when one map islocalized with respect to another. Rather than searching for a relativeorientation of the frames that minimizes the distance between featurepoints of the two images, an initial alignment of the two frames may bemade by identifying feature points with similar descriptors. Alignmentof the image frames may be based on aligning points with similardescriptors, which may entail less processing than computing analignment of all the feature points in the images.

The descriptors may be computed as a mapping of the feature points or,in some embodiments a mapping of a patch of an image around a featurepoint, to a descriptor. The descriptor may be a numeric quantity. U.S.patent application Ser. No. 16/190,948 describes computing descriptorsfor feature points and is hereby incorporated herein by reference in itsentirety.

In the example of FIG. 14, a descriptor 1130 is computed for eachfeature point in each image frame. Based on the descriptors and/or thefeature points and/or the image itself, the image frame may beidentified as a key frame 1140. In the embodiment illustrated, a keyframe is an image frame meeting certain criteria that is then selectedfor further processing. In making a tracking map, for example, imageframes that add meaningful information to the map may be selected as keyframes that are integrated into the map. On the other hand, image framesthat substantially overlap a region for which an image frame has alreadybeen integrated into the map may be discarded such that they do notbecome key frames. Alternatively or additionally, key frames may beselected based on the number and/or type of feature points in the imageframe. In the embodiment of FIG. 14, key frames 1150 selected forinclusion in a tracking map may also be treated as key frames fordetermining a PCF, but different or additional criteria for selectingkey frames for generation of a PCF may be used.

Though FIG. 14 shows that a key frame is used for further processing,information acquired from an image may be processed in other forms. Forexample, the feature points, such as in a key rig, may alternatively oradditionally be processed. Moreover, though a key frame is described asbeing derived from a single image frame, it is not necessary that therebe a one-to-one relationship between a key frame and an acquired imageframe. A key frame, for example, may be acquired from multiple imageframes, such as by stitching together or aggregating the image framessuch that only features appearing in multiple images are retained in thekey frame.

A key frame may include image information and/or metadata associatedwith the image information. In some embodiments, images captured by thecameras 44, 46 (FIG. 9) may be computed into one or more key frames(e.g., key frames 1, 2). In some embodiments, a key frame may include acamera pose. In some embodiments, a key frame may include one or morecamera images captured at the camera pose. In some embodiments, an XRsystem may determine a portion of the camera images captured at thecamera pose as not useful and thus not include the portion in a keyframe. Therefore, using key frames to align new images with earlierknowledge of a scene reduces the use of computational resource of the XRsystem. In some embodiments, a key frame may include an image, and/orimage data, at a location with a direction/angle. In some embodiments, akey frame may include a location and a direction from which one or moremap points may be observed. In some embodiments, a key frame may includea coordinate frame with an ID. U.S. patent application Ser. No.15/877,359 describes key frames and is hereby incorporated herein byreference in its entirety.

Some or all of the key frames 1140 may be selected for furtherprocessing, such as the generation of a persistent pose 1150 for the keyframe. The selection may be based on the characteristics of all, or asubset of, the feature points in the image frame. Those characteristicsmay be determined from processing the descriptors, features and/or imageframe, itself. As a specific example, the selection may be based on acluster of feature points identified as likely to relate to a persistentobject.

Each key frame is associated with a pose of the camera at which that keyframe was acquired. For key frames selected for processing into apersistent pose, that pose information may be saved along with othermetadata about the key frame, such as a WIFI fingerprint and/or GPScoordinates at the time of acquisition and/or at the location ofacquisition.

The persistent poses are a source of information that a device may useto orient itself relative to previously acquired information about thephysical world. For example, if the key frame from which a persistentpose was created is incorporated into a map of the physical world, adevice may orient itself relative to that persistent pose using asufficient number of feature points in the key frame that are associatedwith the persistent pose. The device may align a current image that ittakes of its surroundings to the persistent pose. This alignment may bebased on matching the current image to the image 1110, the features1120, and/or the descriptors 1130 that gave rise to the persistent pose,or any subset of that image or those features or descriptors. In someembodiments, the current image frame that is matched to the persistentpose may be another key frame that has been incorporated into thedevice's tracking map.

Information about a persistent pose may be stored in a format thatfacilitates sharing among multiple applications, which may be executingon the same or different devices. In the example of FIG. 14, some or allof the persistent poses may be reflected as a persistent coordinateframes (PCFs) 1160. Like a persistent pose, a PCF may be associated witha map and may comprise a set of features, or other information, that adevice can use to determine its orientation with respect to that PCF.The PCF may include a transformation that defines its transformationwith respect to the origin of its map, such that, by correlating itsposition to a PCF, the device can determine its position with respect toany objects in the physical world reflected in the map.

As the PCF provides a mechanism for determining locations with respectto the physical objects, an application, such as applications 1180, maydefine positions of virtual objects with respect to one or more PCFs,which serve as anchors for the virtual content 1170. FIG. 14illustrates, for example, that App 1 has associated its virtual content2 with PCF 1.2. Likewise, App 2 has associated its virtual content 3with PCF 1.2. App 1 is also shown associating its virtual content 1 toPCF 4.5, and App 2 is shown associating its virtual content 4 with PCF3. In some embodiments, PCF 3 may be based on Image 3 (not shown), andPCF 4.5 may be based on Image 4 and Image 5 (not shown) analogously tohow PCF 1.2 is based on Image 1 and Image 2. When rendering this virtualcontent, a device may apply one or more transformations to computeinformation, such as the location of the virtual content with respect tothe display of the device and/or the location of physical objects withrespect to the desired location of the virtual content. Using the PCFsas reference may simplify such computations.

In some embodiments, a persistent pose may be a coordinate locationand/or direction that has one or more associated key frames. In someembodiments, a persistent pose may be automatically created after theuser has traveled a certain distance, e.g., three meters. In someembodiments, the persistent poses may act as reference points duringlocalization. In some embodiments, the persistent poses may be stored ina passable world (e.g., the passable world module 538).

In some embodiments, a new PCF may be determined based on a pre-defineddistance allowed between adjacent PCFs. In some embodiments, one or morepersistent poses may be computed into a PCF when a user travels apre-determined distance, e.g., five meters. In some embodiments, PCFsmay be associated with one or more world coordinate frames and/orcanonical coordinate frames, e.g., in the passable world. In someembodiments, PCFs may be stored in a local and/or remote databasedepending on, for example, security settings.

FIG. 15 illustrates a method 4700 of establishing and using apersistence coordinate frame, according to some embodiments. The method4700 may start from capturing (Act 4702) images (e.g., Image 1 and Image2 in FIG. 14) about a scene using one or more sensors of an XR device.Multiple cameras may be used and one camera may generate multipleimages, for example, in a stream.

The method 4700 may include extracting (4704) interest points (e.g., mappoints 702 in FIG. 7, features 1120 in FIG. 14) from the capturedimages, generating (Act 4706) descriptors (e.g., descriptors 1130 inFIG. 14) for the extracted interest points, and generating (Act 4708)key frames (e.g., key frames 1140) based on the descriptors. In someembodiments, the method may compare interest points in the key frames,and form pairs of key frames that share a predetermined amount ofinterest points. The method may reconstruct parts of the physical worldusing individual pairs of key frames. Mapped parts of the physical worldmay be saved as 3D features (e.g., keyrig 704 in FIG. 7). In someembodiments, a selected portion of the pairs of key frames may be usedto build 3D features. In some embodiments, results of the mapping may beselectively saved. Key frames not used for building 3D features may beassociated with the 3D features through poses, for example, representingdistances between key frames with a covariance matrix between poses ofkeyframes. In some embodiments, pairs of key frames may be selected tobuild the 3D features such that distances between each two of the build3D features are within a predetermined distance, which may be determinedto balance the amount of computation needed and the level of accuracy ofa resulting model. Such approaches enable providing a model of thephysical world with the amount of data that is suitable for efficientand accurate computation with an XR system. In some embodiments, acovariance matrix of two images may include covariances between poses ofthe two images (e.g., six degrees of freedom).

The method 4700 may include generating (Act 4710) persistent poses basedon the key frames. In some embodiments, the method may includegenerating the persistent poses based on the 3D features reconstructedfrom pairs of key frames. In some embodiments, a persistent pose may beattached to a 3D feature. In some embodiments, the persistent pose mayinclude a pose of a key frame used to construct the 3D feature. In someembodiments, the persistent pose may include an average pose of keyframes used to construct the 3D feature. In some embodiments, persistentposes may be generated such that distances between neighboringpersistent poses are within a predetermined value, for example, in therange of one meter to five meters, any value in between, or any othersuitable value. In some embodiments, the distances between neighboringpersistent poses may be represented by a covariance matrix of theneighboring persistent poses.

The method 4700 may include generating (Act 4712) PCFs based on thepersistent poses. In some embodiments, a PCF may be attached to a 3Dfeature. In some embodiments, a PCF may be associated with one or morepersistent poses. In some embodiments, a PCF may include a pose of oneof the associated persistent poses. In some embodiments, a PCF mayinclude an average pose of the poses of the associated persistent poses.In some embodiments, PCFs may be generated such that distances betweenneighboring PCFs are within a predetermined value, for example, in therange of three meters to ten meters, any value in between, or any othersuitable value. In some embodiments, the distances between neighboringPCFs may be represented by a covariance matrix of the neighboring PCFs.In some embodiments, PCFs may be exposed to XR applications via, forexample, an application programming interface (API) such that the XRapplications can access a model of the physical world through the PCFswithout accessing the model itself.

The method 4700 may include associating (Act 4714) image data of avirtual object to be displayed by the XR device to at least one of thePCFs. In some embodiments, the method may include computing translationsand orientations of the virtual object with respect to the associatedPCF. It should be appreciated that it is not necessary to associate avirtual object to a PCF generated by the device placing the virtualobject. For example, a device may retrieve saved PCFs in a canonical mapin a cloud and associate a virtual object to a retrieved PCF. It shouldbe appreciated that the virtual object may move with the associated PCFas the PCF is adjusted overtime.

FIG. 16 illustrates the first XR device 12.1 and vision data andalgorithms of a second XR device 12.2 and the server 20, according tosome embodiments. The components illustrated in FIG. 16 may operate toperform some or all of the operations associated with generating,updating, and/or using spatial information, such as persistent poses,persistent coordinate frames, tracking maps, or canonical maps, asdescribed herein. Although not illustrated, the first XR device 12.1 maybe configured the same as the second XR device 12.2. The server 20 mayhave a map storing routine 118, a canonical map 120, a map transmitter122, and a map merge or stitch algorithm 124.

The second XR device 12.2, which may be in the same scene as the firstXR device 12.1, may include a persistent coordinate frame (PCF)integration unit 1300, an application 1302 that generates the image data68 that may be used to render a virtual object, and a frame embeddinggenerator 308 (See FIG. 21). In some embodiments, a map download system126, PCF identification system 128, Map 2, localization module 130,canonical map incorporator 132, canonical map 133, and map publisher 136may be grouped into a passable world unit 1304. The PCF integration unit1300 may be connected to the passable world unit 1304 and othercomponents of the second XR device 12.2 to allow for the retrieval,generation, use, upload, and download of PCFs.

A map, comprising PCFs, may enable more persistence in a changing world.In some embodiments, localizing a tracking map including, for example,matching features for images, may include selecting features thatrepresent persistent content from the map constituted by PCFs, whichenables fast matching and/or localizing. For example, a world wherepeople move into and out of the scene and objects such as doors moverelative to the scene, requires less storage space and transmissionrates, and enables the use of individual PCFs and their relationshipsrelative to one another (e.g., integrated constellation of PCFs) to mapa scene.

In some embodiments, the PCF integration unit 1300 may include PCFs 1306that were previously stored in a data store on a storage unit of thesecond XR device 12.2, a PCF tracker 1308, a persistent pose acquirer1310, a PCF checker 1312, a PCF generation system 1314, a coordinateframe calculator 1316, a persistent pose calculator 1318, and threetransformers, including a tracking map and persistent pose transformer1320, a persistent pose and PCF transformer 1322, and a PCF and imagedata transformer 1324.

In some embodiments, the PCF tracker 1308 may have an on-prompt and anoff-prompt that are selectable by the application 1302. The application1302 may be executable by a processor of the second XR device 12.2 to,for example, display a virtual content. The application 1302 may have acall that switches the PCF tracker 1308 on via the on-prompt. The PCFtracker 1308 may generate PCFs when the PCF tracker 1308 is switched on.The application 1302 may have a subsequent call that can switch the PCFtracker 1308 off via the off-prompt. The PCF tracker 1308 terminates PCFgeneration when the PCF tracker 1308 is switched off.

In some embodiments, the server 20 may include a plurality of persistentposes 1332 and a plurality of PCFs 1330 that have previously been savedin association with a canonical map 120. The map transmitter 122 maytransmit the canonical map 120 together with the persistent poses 1332and/or the PCFs 1330 to the second XR device 12.2. The persistent poses1332 and PCFs 1330 may be stored in association with the canonical map133 on the second XR device 12.2. When Map 2 localizes to the canonicalmap 133, the persistent poses 1332 and the PCFs 1330 may be stored inassociation with Map 2.

In some embodiments, the persistent pose acquirer 1310 may acquire thepersistent poses for Map 2. The PCF checker 1312 may be connected to thepersistent pose acquirer 1310. The PCF checker 1312 may retrieve PCFsfrom the PCFs 1306 based on the persistent poses retrieved by thepersistent pose acquirer 1310. The PCFs retrieved by the PCF checker1312 may form an initial group of PCFs that are used for image displaybased on PCFs.

In some embodiments, the application 1302 may require additional PCFs tobe generated. For example, if a user moves to an area that has notpreviously been mapped, the application 1302 may switch the PCF tracker1308 on. The PCF generation system 1314 may be connected to the PCFtracker 1308 and begin to generate PCFs based on Map 2 as Map 2 beginsto expand. The PCFs generated by the PCF generation system 1314 may forma second group of PCFs that may be used for PCF-based image display.

The coordinate frame calculator 1316 may be connected to the PCF checker1312. After the PCF checker 1312 retrieved PCFs, the coordinate framecalculator 1316 may invoke the head coordinate frame 96 to determine aheadpose of the second XR device 12.2. The coordinate frame calculator1316 may also invoke the persistent pose calculator 1318. The persistentpose calculator 1318 may be directly or indirectly connected to theframe embedding generator 308. In some embodiments, an image/frame maybe designated a key frame after a threshold distance from the previouskey frame, e.g., 3 meters, is traveled. The persistent pose calculator1318 may generate a persistent pose based on a plurality, for examplethree, key frames. In some embodiments, the persistent pose may beessentially an average of the coordinate frames of the plurality of keyframes.

The tracking map and persistent pose transformer 1320 may be connectedto Map 2 and the persistent pose calculator 1318. The tracking map andpersistent pose transformer 1320 may transform Map 2 to the persistentpose to determine the persistent pose at an origin relative to Map 2.

The persistent pose and PCF transformer 1322 may be connected to thetracking map and persistent pose transformer 1320 and further to the PCFchecker 1312 and the PCF generation system 1314. The persistent pose andPCF transformer 1322 may transform the persistent pose (to which thetracking map has been transformed) to the PCFs from the PCF checker 1312and the PCF generation system 1314 to determine the PCFs relative to thepersistent pose.

The PCF and image data transformer 1324 may be connected to thepersistent pose and PCF transformer 1322 and to the data channel 62. ThePCF and image data transformer 1324 transforms the PCFs to the imagedata 68. The rendering engine 30 may be connected to the PCF and imagedata transformer 1324 to display the image data 68 to the user relativeto the PCFs.

The PCF integration unit 1300 may store the additional PCFs that aregenerated with the PCF generation system 1314 within the PCFs 1306. ThePCFs 1306 may be stored relative to persistent poses. The map publisher136 may retrieve the PCFs 1306 and the persistent poses associated withthe PCFs 1306 when the map publisher 136 transmits Map 2 to the server20, the map publisher 136 also transmits the PCFs and persistent posesassociated with Map 2 to the server 20. When the map storing routine 118of the server 20 stores Map 2, the map storing routine 118 may alsostore the persistent poses and PCFs generated by the second viewingdevice 12.2. The map merge or stitch algorithm 124 may create thecanonical map 120 with the persistent poses and PCFs of Map 2 associatedwith the canonical map 120 and stored within the persistent poses 1332and PCFs 1330, respectively.

The first XR device 12.1 may include a PCF integration unit similar tothe PCF integration unit 1300 of the second XR device 12.2. When the maptransmitter 122 transmits the canonical map 120 to the first XR device12.1, the map transmitter 122 may transmit the persistent poses 1332 andPCFs 1330 associated with the canonical map 120 and originating from thesecond XR device 12.2. The first XR device 12.1 may store the PCFs andthe persistent poses within a data store on a storage device of thefirst XR device 12.1. The first XR device 12.1 may then make use of thepersistent poses and the PCFs originating from the second XR device 12.2for image display relative to the PCFs. Additionally or alternatively,the first XR device 12.1 may retrieve, generate, make use, upload, anddownload PCFs and persistent poses in a manner similar to the second XRdevice 12.2 as described above.

In the illustrated example, the first XR device 12.1 generates a localtracking map (referred to hereinafter as “Map 1”) and the map storingroutine 118 receives Map 1 from the first XR device 12.1. The mapstoring routine 118 then stores Map 1 on a storage device of the server20 as the canonical map 120.

The second XR device 12.2 includes a map download system 126, an anchoridentification system 128, a localization module 130, a canonical mapincorporator 132, a local content position system 134, and a mappublisher 136.

In use, the map transmitter 122 sends the canonical map 120 to thesecond XR device 12.2 and the map download system 126 downloads andstores the canonical map 120 as a canonical map 133 from the server 20.

The anchor identification system 128 is connected to the world surfacedetermining routine 78. The anchor identification system 128 identifiesanchors based on objects detected by the world surface determiningroutine 78. The anchor identification system 128 generates a second map(Map 2) using the anchors. As indicated by the cycle 138, the anchoridentification system 128 continues to identify anchors and continues toupdate Map 2. The locations of the anchors are recorded asthree-dimensional data based on data provided by the world surfacedetermining routing 78. The world surface determining routine 78receives images from the real object detection camera 44 and depth datafrom depth sensors 135 to determine the locations of surfaces and theirrelative distance from the depth sensors 135.

The localization module 130 is connected to the canonical map 133 andMap 2. The localization module 130 repeatedly attempts to localize Map 2to the canonical map 133. The canonical map incorporator 132 isconnected to the canonical map 133 and Map 2. When the localizationmodule 130 localizes Map 2 to the canonical map 133, the canonical mapincorporator 132 incorporates the canonical map 133 into anchors of Map2. Map 2 is then updated with missing data that is included in thecanonical map.

The local content position system 134 is connected to Map 2. The localcontent position system 134 may, for example, be a system wherein a usercan locate local content in a particular location within a worldcoordinate frame. The local content then attaches itself to one anchorof Map 2. The local-to-world coordinate transformer 104 transforms thelocal coordinate frame to the world coordinate frame based on thesettings of the local content position system 134. The functioning ofthe rendering engine 30, display system 42, and data channel 62 havebeen described with reference to FIG. 2.

The map publisher 136 uploads Map 2 to the server 20. The map storingroutine 118 of the server 20 then stores Map 2 within a storage mediumof the server 20.

The map merge or stitch algorithm 124 merges or stitches Map 2 with thecanonical map 120. When more than two maps, for example, three or fourmaps relating to the same or adjacent regions of the physical world,have been stored, the map merge or stitch algorithm 124 merges orstitches all the maps into the canonical map 120 to render a newcanonical map 120. The map transmitter 122 then transmits the newcanonical map 120 to any and all devices 12.1 and 12.2 that are in anarea represented by the new canonical map 120. When the devices 12.1 and12.2 localize their respective maps to the canonical map 120, thecanonical map 120 becomes the promoted map.

FIG. 17 illustrates an example of generating key frames for a map of ascene, according to some embodiments. In the illustrated example, afirst key frame, KF1, is generated for a door on a left wall of theroom. A second key frame, KF2, is generated for an area in a cornerwhere a floor, the left wall, and a right wall of the room meet. A thirdkey frame, KF3, is generated for an area of a window on the right wallof the room. A fourth key frame, KF4, is generated for an area at a farend of a rug on a floor of the wall. A fifth key frame, KF5, isgenerated for an area of the rug closest to the user.

FIG. 18 illustrates an example of generating persistent poses for themap of FIG. 17, according to some embodiments. In some embodiments, anew persistent pose is created when the device measures a thresholddistance traveled, and/or when an application requests a new persistentpose (PP). In some embodiments, the threshold distance may be 3 meters,5 meters, 20 meters, or any other suitable distance. Selecting a smallerthreshold distance (e.g., 1 m) may result in an increase in compute loadsince a larger number of PPs may be created and managed compared tolarger threshold distances. Selecting a larger threshold distance (e.g.,40 m) may result in increased virtual content placement error since asmaller number of PPs would be created, which would result in fewer PCFsbeing created, which means the virtual content attached to the PCF couldbe a relatively large distance (e.g., 30 m) away from the PCF, and errorincreases with increasing distance from a PCF to the virtual content.

In some embodiments, a PP may be created at the start of a new session.This initial PP may be thought of as zero, and can be visualized as thecenter of a circle that has a radius equal to the threshold distance.When the device reaches the perimeter of the circle, and, in someembodiments, an application requests a new PP, a new PP may be placed atthe current location of the device (at the threshold distance). In someembodiments, a new PP will not be created at the threshold distance ifthe device is able to find an existing PP within the threshold distancefrom the device's new position. In some embodiments, when a new PP(e.g., PP1150 in FIG. 14) is created, the device attaches one or more ofthe closest key frames to the PP. In some embodiments, the location ofthe PP relative to the key frames may be based on the location of thedevice at the time a PP is created. In some embodiments, a PP will notbe created when the device travels a threshold distance unless anapplication requests a PP.

In some embodiments, an application may request a PCF from the devicewhen the application has virtual content to display to the user. The PCFrequest from the application may trigger a PP request, and a new PPwould be created after the device travels the threshold distance. FIG.18 illustrates a first persistent pose PP1 which may have the closestkey frames, (e.g., KF1, KF2, and KF3) attached by, for example,computing relative poses between the key frames to the persistent pose.FIG. 18 also illustrates a second persistent pose PP2 which may have theclosest key frames (e.g., KF4 and KF5) attached.

FIG. 19 illustrates an example of generating a PCF for the map of FIG.17, according to some embodiments. In the illustrated example, a PCF mayinclude PP1 and PP2. As described above, the PCF may be used fordisplaying image data relative to the PCF. In some embodiments, each PCFmay have coordinates in another coordinate frame (e.g., a worldcoordinate frame) and a PCF descriptor, for example, uniquelyidentifying the PCF. In some embodiments, the PCF descriptor may becomputed based on feature descriptors of features in frames associatedwith the PCF. In some embodiments, various constellations of PCFs may becombined to represent the real world in a persistent manner and thatrequires less data and less transmission of data.

FIGS. 20A to 20C are schematic diagrams illustrating an example ofestablishing and using a persistent coordinate frame. FIG. 20A shows twousers 4802A, 4802B with respective local tracking maps 4804A, 4804B thathave not localized to a canonical map. The origins 4806A, 4806B forindividual users are depicted by the coordinate system (e.g., a worldcoordinate system) in their respective areas. These origins of eachtracking map may be local to each user, as the origins are dependent onthe orientation of their respective devices when tracking was initiated.

As the sensors of the user device scan the environment, the device maycapture images that, as described above in connection with FIG. 14, maycontain features representing persistent objects such that those imagesmay be classified as key frames, from which a persistent pose may becreated. In this example, the tracking map 4802A includes a persistentpose (PP) 4808A; the tracking 4802B includes a PP 4808B.

Also as described above in connection with FIG. 14, some of the PP's maybe classified as PCFs which are used to determine the orientation ofvirtual content for rendering it to the user. FIG. 20B shows that XRdevices worn by respective users 4802A, 4802B may create local PCFs4810A, 4810B based on the PP 4808A, 4808B. FIG. 20C shows thatpersistent content 4812A, 4812B (e.g., a virtual content) may beattached to the PCFs 4810A, 4810B by respective XR devices.

In this example, virtual content may have a virtual content coordinateframe, that may be used by an application generating virtual content,regardless of how the virtual content should be displayed. The virtualcontent, for example, may be specified as surfaces, such as triangles ofa mesh, at particular locations and angles with respect to the virtualcontent coordinate frame. To render that virtual content to a user, thelocations of those surfaces may be determined with respect to the userthat is to perceive the virtual content.

Attaching virtual content to the PCFs may simplify the computationinvolved in determining locations of the virtual content with respect tothe user. The location of the virtual content with respect to a user maybe determined by applying a series of transformations. Some of thosetransformations may change, and may be updated frequently. Others ofthose transformations may be stable and may be updated in frequently ornot at all. Regardless, the transformations may be applied withrelatively low computational burden such that the location of thevirtual content can be updated with respect to the user frequently,providing a realistic appearance to the rendered virtual content.

In the example of FIGS. 20A-20C, user 1's device has a coordinate systemthat can be related to the coordinate system that defines the origin ofthe map by the transformation rig1_T_w1. User 2's device has a similartransformation rig2_T_w2. These transformations may be expressed as sixdegrees of transformation, specifying translation and rotation to alignthe device coordinate systems with the map coordinate systems. In someembodiments, the transformation may be expressed as two separatetransformations, one specifying translation and the other specifyingrotation. Accordingly, it should be appreciated that the transformationsmay be expressed in a form that simplifies computation or otherwiseprovides an advantage.

Transformations between the origins of the tracking maps and the PCFsidentified by the respective user devices are expressed as pcf1_T_w1 andpcf2_T_w2. In this example the PCF and the PP are identical, such thatthe same transformation also characterizes the PP's.

The location of the user device with respect to the PCF can therefore becomputed by the serial application of these transformations, such asrig1_T_pcf1=(rig1_T_w1)*(pcf1_T_w1).

As shown in FIG. 20C, the virtual content is located with respect to thePCFs, with a transformation of obj1_T_pcf1. This transformation may beset by an application generating the virtual content that may receiveinformation from a world reconstruction system describing physicalobjects with respect to the PCF. To render the virtual content to theuser, a transformation to the coordinate system of the user's device iscomputed, which may be computed by relating the virtual contentcoordinate frame to the origin of the tracking map through thetransformation obj1_t_w1=(obj1_T_pcf1)*(pcf1_T_w1). That transformationmay then be related to the user's device through further transformationrig1_T_w1.

The location of the virtual content may change, based on output from anapplication generating the virtual content. When that changes, theend-to-end transformation, from a source coordinate system to adestination coordinate system, may be recomputed. Additionally, thelocation and/or headpose of the user may change as the user moves. As aresult, the transformation rig1_T_w1 may change, as would any end-to-endtransformation that depends on the location or headpose of the user.

The transformation rig1_T_w1 may be updated with motion of the userbased on tracking the position of the user with respect to stationaryobjects in the physical world. Such tracking may be performed by aheadphone tacking component processing a sequence of images, asdescribed above, or other component of the system. Such updates may bemade by determining pose of the user with respect to a stationary frameof reference, such as a PP.

In some embodiments, the location and orientation of a user device maybe determined relative to the nearest persistent pose, or, in thisexample, a PCF, as the PP is used as a PCF. Such a determination may bemade by identifying in current images captured with sensors on thedevice feature points that characterize the PP. Using image processingtechniques, such as stereoscopic image analysis, the location of thedevice with respect to those feature points may be determined. From thisdata, the system could calculate the change in transformation associatedwith the user's motions based on the relationshiprig1_T_pcf1=(rig1_T_w1)*(pcf1_T_w1).

A system may determine and apply transformations in an order that iscomputationally efficient. For example, the need to compute rig1_T_w1from a measurement yielding rig1_T_pcf1 might be avoided by trackinguser pose and defining the location of virtual content relative to thePP or a PCF built on a persistent pose. In this way the transformationfrom a source coordinate system of the virtual content to thedestination coordinate system of the user's device may be based on themeasured transformation according to the expression(rig1_T_pcf1)*(obj1_t_pcf1), with the first transformation beingmeasured by the system and the latter transformation being supplied byan application specifying virtual content for rendering. In embodimentsin which the virtual content is positioned with respect to the origin ofthe map, the end-to-end transformation may relate the virtual objectcoordinate system to the PCF coordinate system based on a furthertransformation between the map coordinates and the PCF coordinates. Inembodiments in which the virtual content is positioned with respect to adifferent PP or PCF than the one against which user position is beingtracked, a transformation between the two may be applied. Such atransformation may be fixed and may be determined, for example, from amap in which both appear.

A transform-based approach may be implemented, for example, in a devicewith components that process sensor data to build a tracking map. Aspart of that process, those components may identify feature points thatmay be used as persistent poses, which in turn may be turned into PCFs.Those components may limit the number of persistent poses generated forthe map, to provide a suitable spacing between persistent poses, whileallowing the user, regardless of location in the physical environment,to be close enough to a persistent pose location to accurately computethe user's pose, as described above in connection with FIGS. 17-19. Asthe closest persistent pose to a user is updated, as a result of usermovement, refinements to the tracking map or other causes, any of thetransformations that are used to compute the location of virtual contentrelative to the user that depend on the location of the PP (or PCF ifbeing used) may be updated and stored for use, at least until the usermoves away from that persistent pose. Nonetheless, by computing andstoring transformations, the computational burden each time the locationof virtual content is update may be relatively low, such that it may beperformed with relatively low latency.

FIGS. 20A-20C illustrate positioning with respect to a tracking map, andeach device had its own tracking map. However, transformations may begenerated with respect to any map coordinate system. Persistence ofcontent across user sessions of an XR system may be achieved by using apersistent map. Shared experiences of users may also be facilitated byusing a map to which multiple user devices may be oriented.

In some embodiments, described in greater detail below, the location ofvirtual content may be specified in relation to coordinates in acanonical map, formatted such that any of multiple devices may use themap. Each device might maintain a tracking map and may determine thechange of pose of the user with respect to the tracking map. In thisexample, a transformation between the tracking map and the canonical mapmay be determined through a process of “localization”—which may beperformed by matching structures in the tracking map (such as one ormore persistent poses) to one or more structures of the canonical map(such as one or more PCFs).

Described in greater below are techniques for creating and usingcanonical maps in this way.

Deep Key Frame

Techniques as described herein rely on comparison of image frames. Forexample, to establish the position of a device with respect to atracking map, a new image may be captured with sensors worn by the userand an XR system may search, in a set of images that were used to createthe tracking map, images that share at least a predetermined amount ofinterest points with the new image. As an example of another scenarioinvolving comparisons of image frames, a tracking map might be localizedto a canonical map by first finding image frames associated with apersistent pose in the tracking map that is similar to an image frameassociated with a PCF in the canonical map. Alternatively, atransformation between two canonical maps may be computed by firstfinding similar image frames in the two maps.

Deep key frames provide a way to reduce the amount of processingrequired to identify similar image frames. For example, in someembodiments, the comparison may be between image features in a new 2Dimage (e.g., “2D features”) and 3D features in the map. Such acomparison may be made in any suitable way, such as by projecting the 3Dimages into a 2D plane. A conventional method such as Bag of Words (BoW)searches the 2D features of a new image in a database including all 2Dfeatures in a map, which may require significant computing resourcesespecially when a map represents a large area. The conventional methodthen locates the images that share at least one of the 2D features withthe new image, which may include images that are not useful for locatingmeaningful 3D features in the map. The conventional method then locates3D features that are not meaningful with respect to the 2D features inthe new image.

The inventors have recognized and appreciated techniques to retrieveimages in the map using less memory resource (e.g., a quarter of thememory resource used by BoW), higher efficiency (e.g., 2.5 ms processingtime for each key frame, 100 μs for comparing against 500 key frames),and higher accuracy (e.g., 20% better retrieval recall than BoW for1024-dimensional model, 5% better retrieval recall than BoW for256-dimensional model).

To reduce computation, a descriptor may be computed for an image framethat may be used to compare an image frame to other image frames. Thedescriptors may be stored instead of or in addition to the image framesand feature points. In a map in which persistent poses and/or PCFs maybe generated from image frames, the descriptor of the image frame orframes from which each persistent pose or PCF was generated may bestored as part of the persistent pose and/or PCF.

In some embodiments, the descriptor may be computed as a function offeature points in the image frame. In some embodiments, a neural networkis configured to compute a unique frame descriptor to represent animage. The image may have a resolution higher than 1 Megabyte such thatenough details of a 3D environment within a field-of-view of a deviceworn by a user is captured in the image. The frame descriptor may bemuch shorter, such as a string of numbers, for example, in the range of128 Bytes to 512 Bytes or any number in between.

In some embodiments, the neural network is trained such that thecomputed frame descriptors indicate similarity between images. Images ina map may be located by identifying, in a database comprising imagesused to generate the map, the nearest images that may have framedescriptors within a predetermined distance to a frame descriptor for anew image. In some embodiments, the distances between images may berepresented by a difference between the frame descriptors of the twoimages.

FIG. 21 is a block diagram illustrating a system for generating adescriptor for an individual image, according to some embodiments. Inthe illustrated example, a frame embedding generator 308 is shown. Theframe embedding generator 308, in some embodiments, may be used withinthe server 20, but may alternatively or additionally execute in whole orin part within one of the XR devices 12.1 and 12.2, or any other deviceprocessing images for comparison to other images.

In some embodiments, the frame embedding generator may be configured togenerate a reduced data representation of an image from an initial size(e.g., 76,800 bytes) to a final size (e.g., 256 bytes) that isnonetheless indicative of the content in the image despite a reducedsize. In some embodiments, the frame embedding generator may be used togenerate a data representation for an image which may be a key frame ora frame used in other ways. In some embodiments, the frame embeddinggenerator 308 may be configured to convert an image at a particularlocation and orientation into a unique string of numbers (e.g., 256bytes). In the illustrated example, an image 320 taken by an XR devicemay be processed by feature extractor 324 to detect interest points 322in the image 320. Interest points may be or may not be derived fromfeature points identified as described above for features 1120 (FIG. 14)or as otherwise described herein. In some embodiments, interest pointsmay be represented by descriptors as described above for descriptors1130 (FIG. 14), which may be generated using a deep sparse featuremethod. In some embodiments, each interest point 322 may be representedby a string of numbers (e.g., 32 bytes). There may, for example, be nfeatures (e.g., 100) and each feature is represented by a string of 32bytes.

In some embodiments, the frame embedding generator 308 may include aneural network 326. The neural network 326 may include a multi-layerperceptron unit 312 and a maximum (max) pool unit 314. In someembodiments, the multi-layer perceptron (MLP) unit 312 may comprise amulti-layer perceptron, which may be trained. In some embodiments, theinterest points 322 (e.g., descriptors for the interest points) may bereduced by the multi-layer perceptron 312, and may output as weightedcombinations 310 of the descriptors. For example, the MLP may reduce nfeatures to m feature that is less than n features.

In some embodiments, the MLP unit 312 may be configured to performmatrix multiplication. The multi-layer perceptron unit 312 receives theplurality of interest points 322 of an image 320 and converts eachinterest point to a respective string of numbers (e.g., 256). Forexample, there may be 100 features and each feature may be representedby a string of 256 numbers. A matrix, in this example, may be createdhaving 100 horizontal rows and 256 vertical columns. Each row may have aseries of 256 numbers that vary in magnitude with some being smaller andothers being larger. In some embodiments, the output of the MLP may bean n×256 matrix, where n represents the number of interest pointsextracted from the image. In some embodiments, the output of the MLP maybe an m×256 matrix, where m is the number of interest points reducedfrom n.

In some embodiments, the MLP 312 may have a training phase, during whichmodel parameters for the MLP are determined, and a use phase. In someembodiments, the MLP may be trained as illustrated in FIG. 25. The inputtraining data may comprise data in sets of three, the set of threecomprising 1) a query image, 2) a positive sample, and 3) a negativesample. The query image may be considered the reference image.

In some embodiments, the positive sample may comprise an image that issimilar to the query image. For example, in some embodiments, similarmay be having the same object in both the query and positive sampleimage but viewed from a different angle. In some embodiments, similarmay be having the same object in both the query and positive sampleimages but having the object shifted (e.g., left, right, up, down)relative to the other image.

In some embodiments, the negative sample may comprise an image that isdissimilar to the query image. For example, in some embodiments, adissimilar image may not contain any objects that are prominent in thequery image or may contain only a small portion of a prominent object inthe query image (e.g., <10%, 1%). A similar image, in contrast, may havemost of an object (e.g., >50%, or >75%) in the query image, for example.

In some embodiments, interest points may be extracted from the images inthe input training data and may be converted to feature descriptors.These descriptors may be computed both for the training images as shownin FIG. 25 and for extracted features in operation of frame embeddinggenerator 308 of FIG. 21. In some embodiments, a deep sparse feature(DSF) process may be used to generate the descriptors (e.g., DSFdescriptors) as described in U.S. patent application Ser. No.16/190,948. In some embodiments, DSF descriptors are n×32 dimension. Thedescriptors may then be passed through the model/MLP to create a256-byte output. In some embodiments, the model/MLP may have the samestructure as MLP 312 such that once the model parameters are set throughtraining, the resulting trained MLP may be used as MLP 312.

In some embodiments, the feature descriptors (e.g., the 256-byte outputfrom the MLP model) may then be sent to a triplet margin loss module(which may only be used during the training phase, not during use phaseof the MLP neural network). In some embodiments, the triplet margin lossmodule may be configured to select parameters for the model so as toreduce the difference between the 256-byte output from the query imageand the 256-byte output from the positive sample, and to increase thedifference between the 256-byte output from the query image and the256-byte output from the negative sample. In some embodiments, thetraining phase may comprise feeding a plurality of triplet input imagesinto the learning process to determine model parameters. This trainingprocess may continue, for example, until the differences for positiveimages is minimized and the difference for negative images is maximizedor until other suitable exit criteria are reached.

Referring back to FIG. 21, the frame embedding generator 308 may includea pooling layer, here illustrated as maximum (max) pool unit 314. Themax pool unit 314 may analyze each column to determine a maximum numberin the respective column. The max pool unit 314 may combine the maximumvalue of each column of numbers of the output matrix of the MLP 312 intoa global feature string 316 of, for example, 256 numbers. It should beappreciated that images processed in XR systems might, desirably, havehigh-resolution frames, with potentially millions of pixels. The globalfeature string 316 is a relatively small number that takes up relativelylittle memory and is easily searchable compared to an image (e.g., witha resolution higher than 1 Megabyte). It is thus possible to search forimages without analyzing each original frame from the camera and it isalso cheaper to store 256 bytes instead of complete frames.

FIG. 22 is a flow chart illustrating a method 2200 of computing an imagedescriptor, according to some embodiments. The method 2200 may startfrom receiving (Act 2202) a plurality of images captured by an XR deviceworn by a user. In some embodiments, the method 2200 may includedetermining (Act 2204) one or more key frames from the plurality ofimages. In some embodiments, Act 2204 may be skipped and/or may occurafter step 2210 instead.

The method 2200 may include identifying (Act 2206) one or more interestpoints in the plurality of images with an artificial neural network, andcomputing (Act 2208) feature descriptors for individual interest pointswith the artificial neural network. The method may include computing(Act 2210), for each image, a frame descriptor to represent the imagebased, at least in part, on the computed feature descriptors for theidentified interest points in the image with the artificial neuralnetwork.

FIG. 23 is a flow chart illustrating a method 2300 of localization usingimage descriptors, according to some embodiments. In this example, a newimage frame, depicting the current location of the XR device may becompared to image frames stored in connection with points in a map (suchas a persistent pose or a PCF as described above). The method 2300 maystart from receiving (Act 2302) a new image captured by an XR deviceworn by a user. The method 2300 may include identifying (Act 2304) oneor more nearest key frames in a database comprising key frames used togenerate one or more maps. In some embodiments, a nearest key frame maybe identified based on coarse spatial information and/or previouslydetermined spatial information. For example, coarse spatial informationmay indicate that the XR device is in a geographic region represented bya 50 m×50 m area of a map. Image matching may be performed only forpoints within that area. As another example, based on tracking, the XRsystem may know that an XR device was previously proximate a firstpersistent pose in the map and was moving in a direction of a secondpersistent pose in the map. That second persistent pose may beconsidered the nearest persistent pose and the key frame stored with itmay be regarded as the nearest key frame. Alternatively or additionally,other metadata, such as GPS data or WIFI fingerprints, may be used toselect a nearest key frame or set of nearest key frames.

Regardless of how the nearest key frames are selected, frame descriptorsmay be used to determine whether the new image matches any of the framesselected as being associated with a nearby persistent pose. Thedetermination may be made by comparing a frame descriptor of the newimage with frame descriptors of the closest key frames, or a subset ofkey frames in the database selected in any other suitable way, andselecting key frames with frame descriptors that are within apredetermined distance of the frame descriptor of the new image. In someembodiments, a distance between two frame descriptors may be computed byobtaining the difference between two strings of numbers that mayrepresent the two frame descriptors. In embodiments in which the stringsare processed as strings of multiple quantities, the difference may becomputed as a vector difference.

Once a matching image frame is identified, the orientation of the XRdevice relative to that image frame may be determined. The method 2300may include performing (Act 2306) feature matching against 3D featuresin the maps that correspond to the identified nearest key frames, andcomputing (Act 2308) pose of the device worn by the user based on thefeature matching results. In this way, the computationally intensivematching of features points in two images may be performed for as few asone image that has already been determined to be a likely match for thenew image.

FIG. 24 is a flow chart illustrating a method 2400 of training a neuralnetwork, according to some embodiments. The method 2400 may start fromgenerating (Act 2402) a dataset comprising a plurality of image sets.Each of the plurality of image sets may include a query image, apositive sample image, and a negative sample image. In some embodiments,the plurality of image sets may include synthetic recording pairsconfigured to, for example, teach the neural network basic informationsuch as shapes. In some embodiments, the plurality of image sets mayinclude real recording pairs, which may be recorded from a physicalworld.

In some embodiments, inliers may be computed by fitting a fundamentalmatrix between two images. In some embodiments, sparse overlap may becomputed as the intersection over union (IoU) of interest points seen inboth images. In some embodiments, a positive sample may include at leasttwenty interest points, serving as inliers, that are the same as in thequery image. A negative sample may include less than ten inlier points.A negative sample may have less than half of the sparse pointsoverlapping with the sparse points of the query image.

The method 2400 may include computing (Act 2404), for each image set, aloss by comparing the query image with the positive sample image and thenegative sample image. The method 2400 may include modifying (Act 2406)the artificial neural network based on the computed loss such that adistance between a frame descriptor generated by the artificial neuralnetwork for the query image and a frame descriptor for the positivesample image is less than a distance between the frame descriptor forthe query image and a frame descriptor for the negative sample image.

It should be appreciated that although methods and apparatus configuredto generate global descriptors for individual images are describedabove, methods and apparatus may be configured to generate descriptorsfor individual maps. For example, a map may include a plurality of keyframes, each of which may have a frame descriptor as described above. Amax pool unit may analyze the frame descriptors of the map's key framesand combines the frame descriptors into a unique map descriptor for themap.

Further, it should be appreciated that other architectures may be usedfor processing as described above. For example, separate neural networksare described for generating DSF descriptors and frame descriptors. Suchan approach is computationally efficient. However, in some embodiments,the frame descriptors may be generated from selected feature points,without first generating DSF descriptors.

Ranking and Merging Maps

Described herein are methods and apparatus for ranking and merging orstitching a plurality of environment maps in an X Reality (XR) system.Map merging or stitching may enable maps representing overlappingportions of the physical world to be combined to represent a largerarea. Ranking maps may enable efficiently performing techniques asdescribed herein, including map merging or stitching, that involveselecting a map from a set of maps based on similarity. In someembodiments, for example, a set of canonical maps formatted in a waythat they may be accessed by any of a number of XR devices, may bemaintained by the system. These canonical maps may be formed by mergingor stitching selected tracking maps from those devices with othertracking maps or previously stored canonical maps. The canonical mapsmay be ranked, for example, for use in selecting one or more canonicalmaps to merge or stitch with a new tracking map and/or to select one ormore canonical maps from the set to use within a device.

To provide realistic XR experiences to users, the XR system must knowthe user's physical surroundings in order to correctly correlatelocations of virtual objects in relation to real objects. Informationabout a user's physical surroundings may be obtained from an environmentmap for the user's location.

The inventors have recognized and appreciated that an XR system couldprovide an enhanced XR experience to multiple users sharing a sameworld, comprising real and/or virtual content, by enabling efficientsharing of environment maps of the real/physical world collected bymultiple users, whether those users are present in the world at the sameor different times. However, there are significant challenges inproviding such a system. Such a system may store multiple maps generatedby multiple users and/or the system may store multiple maps generated atdifferent times. For operations that might be performed with apreviously generated map, such as localization, for example as describedabove, substantial processing may be required to identify a relevantenvironment map of a same world (e.g., same real-world location) fromall the environment maps collected in an XR system. In some embodiments,there may only be a small number of environment maps a device couldaccess, for example for localization. In some embodiments, there may bea large number of environment maps a device could access. The inventorshave recognized and appreciated techniques to quickly and accuratelyrank the relevance of environment maps out of all possible environmentmaps, such as the universe of all canonical maps 120 in FIG. 28, forexample. A high-ranking map may then be selected for further processing,such as to render virtual objects on a user display realisticallyinteracting with the physical world around the user or merging orstitching map data collected by that user with stored maps to createlarger or more accurate maps.

In some embodiments, a stored map that is relevant to a task for a userat a location in the physical world may be identified by filteringstored maps based on multiple criteria. Those criteria may indicatecomparisons of a tracking map, generated by the wearable device of theuser in the location, to candidate environment maps stored in adatabase. The comparisons may be performed based on metadata associatedwith the maps, such as a Wi-Fi fingerprint detected by the devicegenerating the map and/or set of BSS ID's to which the device wasconnected while forming the map. The comparisons may also be performedbased on compressed or uncompressed content of the map. Comparisonsbased on a compressed representation may be performed, for example, bycomparison of vectors computed from map content. Comparisons based onun-compressed maps may be performed, for example, by localizing thetracking map within the stored map, or vice versa. Multiple comparisonsmay be performed in an order based on computation time needed to reducethe number of candidate maps for consideration, with comparisonsinvolving less computation being performed earlier in the order thanother comparisons requiring more computation.

FIG. 26 depicts an AR system 800 configured to rank and merge or stitchone or more environment maps, according to some embodiments. The ARsystem may include a passable world model 802 of an AR device.Information to populate the passable world model 802 may come fromsensors on the AR device, which may include computer executableinstructions stored in a processor 804 (e.g., a local data processingmodule 570 in FIG. 4), which may perform some or all of the processingto convert sensor data into a map. Such a map may be a tracking map, asit can be built as sensor data is collected as the AR device operates ina region. Along with that tracking map, area attributes may be suppliedso as to indicate the area that the tracking map represents. These areaattributes may be a geographic location identifier, such as coordinatespresented as latitude and longitude or an ID used by the AR system torepresent a location. Alternatively or additionally, the area attributesmay be measured characteristics that have a high likelihood of beingunique for that area. The area attributes, for example, may be derivedfrom parameters of wireless networks detected in the area. In someembodiments, the area attribute may be associated with a unique addressof an access-point the AR system is nearby and/or connected to. Forexample, the area attribute may be associated with a MAC address orbasic service set identifiers (BSSIDs) of a 5G base station/router, aWi-Fi router, and the like.

In the example of FIG. 26, the tracking maps may be merged or stitchedwith other maps of the environment. A map rank portion 806 receivestracking maps from the device PW 802 and communicates with a mapdatabase 808 to select and rank environment maps from the map database808. Higher ranked, selected maps are sent to a map merge or stitchportion 810.

The map merge or stitch portion 810 may perform merge or stitchprocessing on the maps sent from the map rank portion 806. Mergeprocessing may entail merging or stitching the tracking map with some orall of the ranked maps and transmitting the new, merged or stitched mapsto a passable world model 812. The map merge or stitch portion may mergeor stitch maps by identifying maps that depict overlapping portions ofthe physical world. Those overlapping portions may be aligned such thatinformation in both maps may be aggregated into a final map. Canonicalmaps may be merged or stitched with other canonical maps and/or trackingmaps.

The aggregation may entail extending one map with information fromanother map. Alternatively or additionally, aggregation may entailadjusting the representation of the physical world in one map, based oninformation in another map. A later map, for example, may reveal thatobjects giving rise to feature points have moved, such that the map maybe updated based on later information. Alternatively, two maps maycharacterize the same region with different feature points andaggregating may entail selecting a set of feature points from the twomaps to better represent that region. Regardless of the specificprocessing that occurs in the merging or stitching process, in someembodiments, PCFs from all maps that are merged or stitched may beretained, such that applications positioning content with respect tothem may continue to do so. In some embodiments, merging or stitching ofmaps may result in redundant persistent poses, and some of thepersistent poses may be deleted. When a PCF is associated with apersistent pose that is to be deleted, merging or stitching maps mayentail modifying the PCF to be associated with a persistent poseremaining in the map after merging or stitching.

In some embodiments, as maps are extended and or updated, they may berefined. Refinement may entail computation to reduce internalinconsistency between feature points that likely represent the sameobject in the physical world. Inconsistency may result from inaccuraciesin the poses associated with key frames supplying feature points thatrepresent the same objects in the physical world. Such inconsistency mayresult, for example, from an XR device computing poses relative to atracking map, which in turn is built based on estimating poses, suchthat errors in pose estimation accumulate, creating a “drift” in poseaccuracy over time. By performing a bundle adjustment or other operationto reduce inconsistencies of the feature points from multiple keyframes, the map may be refined.

Upon a refinement, the location of a persistent point relative to theorigin of a map may change. Accordingly, the transformation associatedwith that persistent point, such as a persistent pose or a PCF, maychange. In some embodiments, the XR system, in connection with maprefinement (whether as part of a merge or stitch operation or performedfor other reasons) may re-compute transformations associated with anypersistent points that have changed. These transformations might bepushed from a component computing the transformations to a componentusing the transformation such that any uses of the transformations maybe based on the updated location of the persistent points.

Passable world model 812 may be a cloud model, which may be shared bymultiple AR devices. Passable world model 812 may store or otherwisehave access to the environment maps in map database 808. In someembodiments, when a previously computed environment map is updated, theprior version of that map may be deleted so as to remove out of datemaps from the database. In some embodiments, when a previously computedenvironment map is updated, the prior version of that map may bearchived enabling retrieving/viewing prior versions of an environment.In some embodiments, permissions may be set such that only AR systemshaving certain read/write access may trigger prior versions of mapsbeing deleted/archived.

These environment maps created from tracking maps supplied by one ormore AR devices/systems may be accessed by AR devices in the AR system.The map rank portion 806 also may be used in supplying environment mapsto an AR device. The AR device may send a message requesting anenvironment map for its current location, and map rank portion 806 maybe used to select and rank environment maps relevant to the requestingdevice.

In some embodiments, the AR system 800 may include a downsample portion814 configured to receive the merged or stitched maps from the cloud PW812. The received merged or stitched maps from the cloud PW 812 may bein a storage format for the cloud, which may include high resolutioninformation, such as a large number of PCFs per square meter or multipleimage frames or a large set of feature points associated with a PCF. Thedownsample portion 814 may be configured to downsample the cloud formatmaps to a format suitable for storage on AR devices. The device formatmaps may have less data, such as fewer PCFs or less data stored for eachPCF to accommodate the limited local computing power and storage spaceof AR devices.

FIG. 27 is a simplified block diagram illustrating a plurality ofcanonical maps 120 that may be stored in a remote storage medium, forexample, a cloud. Each canonical map 120 may include a plurality ofcanonical map identifiers indicating the canonical map's location withina physical space, such as somewhere on the planet earth. These canonicalmap identifiers may include one or more of the following identifiers:area identifiers represented by a range of longitudes and latitudes,frame descriptors (e.g., global feature string 316 in FIG. 21), Wi-Fifingerprints, feature descriptors (e.g., feature descriptors 310 in FIG.21), and device identities indicating one or more devices thatcontributed to the map.

In the illustrated example, the canonical maps 120 are disposedgeographically in a two-dimensional pattern as they may exist on asurface of the earth. The canonical maps 120 may be uniquelyidentifiable by corresponding longitudes and latitudes because anycanonical maps that have overlapping longitudes and latitudes may bemerged or stitched into a new canonical map.

FIG. 28 is a schematic diagram illustrating a method of selectingcanonical maps, which may be used for localizing a new tracking map toone or more canonical maps, according to some embodiment. The method maystart from accessing (Act 120) a universe of canonical maps 120, whichmay be stored, as an example, in a database in a passable world (e.g.,the passable world module 538). The universe of canonical maps mayinclude canonical maps from all previously visited locations. An XRsystem may filter the universe of all canonical maps to a small subsetor just a single map. It should be appreciated that, in someembodiments, it may not be possible to send all the canonical maps to aviewing device due to bandwidth restrictions. Selecting a subsetselected as being likely candidates for matching the tracking map tosend to the device may reduce bandwidth and latency associated withaccessing a remote database of maps.

The method may include filtering (Act 300) the universe of canonicalmaps based on areas with predetermined size and shapes. In theillustrated example in FIG. 27, each square may represent an area. Eachsquare may cover 50 m×50 m. Each square may have six neighboring areas.In some embodiments, Act 300 may select at least one matching canonicalmap 120 covering longitude and latitude that include that longitude andlatitude of the position identifier received from an XR device, as longas at least one map exists at that longitude and latitude. In someembodiments, the Act 300 may select at least one neighboring canonicalmap covering longitude and latitude that are adjacent the matchingcanonical map. In some embodiments, the Act 300 may select a pluralityof matching canonical maps and a plurality of neighboring canonicalmaps. The Act 300 may, for example, reduce the number of canonical mapsapproximately ten times, for example, from thousands to hundreds to forma first filtered selection. Alternatively or additionally, criteriaother than latitude and longitude may be used to identify neighboringmaps. An XR device, for example, may have previously localized with acanonical map in the set as part of the same session. A cloud servicemay retain information about the XR device, including maps previouslylocalized to. In this example, the maps selected at Act 300 may includethose that cover an area adjacent to the map to which the XR devicelocalized to.

The method may include filtering (Act 302) the first filtered selectionof canonical maps based on Wi-Fi fingerprints. The Act 302 may determinea latitude and longitude based on a Wi-Fi fingerprint received as partof the position identifier from an XR device. The Act 302 may comparethe latitude and longitude from the Wi-Fi fingerprint with latitude andlongitude of the canonical maps 120 to determine one or more canonicalmaps that form a second filtered selection. The Act 302 may reduce thenumber of canonical maps approximately ten times, for example, fromhundreds to tens of canonical maps (e.g., 50) that form a secondselection. For example, a first filtered selection may include 130canonical maps and the second filtered selection may include 50 of the130 canonical maps and may not include the other 80 of the 130 canonicalmaps.

The method may include filtering (Act 304) the second filtered selectionof canonical maps based on key frames. The Act 304 may compare datarepresenting an image captured by an XR device with data representingthe canonical maps 120. In some embodiments, the data representing theimage and/or maps may include feature descriptors (e.g., DSF descriptorsin FIG. 25) and/or global feature strings (e.g., 316 in FIG. 21). TheAct 304 may provide a third filtered selection of canonical maps. Insome embodiments, the output of Act 304 may only be five of the 50canonical maps identified following the second filtered selection, forexample. The map transmitter 122 then transmits the one or morecanonical maps based on the third filtered selection to the viewingdevice. The Act 304 may reduce the number of canonical maps forapproximately ten times, for example, from tens to single digits ofcanonical maps (e.g., 5) that form a third selection. In someembodiments, an XR device may receive canonical maps in the thirdfiltered selection, and attempt to localize into the received canonicalmaps.

For example, the Act 304 may filter the canonical maps 120 based on theglobal feature strings 316 of the canonical maps 120 and the globalfeature string 316 that is based on an image that is captured by theviewing device (e.g., an image that may be part of the local trackingmap for a user). Each one of the canonical maps 120 in FIG. 27 thus hasone or more global feature strings 316 associated therewith. In someembodiments, the global feature strings 316 may be acquired when an XRdevice submits images or feature details to the cloud and the cloudprocesses the image or feature details to generate global featurestrings 316 for the canonical maps 120.

In some embodiments, the cloud may receive feature details of alive/new/current image captured by a viewing device, and the cloud maygenerate a global feature string 316 for the live image. The cloud maythen filter the canonical maps 120 based on the live global featurestring 316. In some embodiments, the global feature string may begenerated on the local viewing device. In some embodiments, the globalfeature string may be generated remotely, for example on the cloud. Insome embodiments, a cloud may transmit the filtered canonical maps to anXR device together with the global feature strings 316 associated withthe filtered canonical maps. In some embodiments, when the viewingdevice localizes its tracking map to the canonical map, it may do so bymatching the global feature strings 316 of the local tracking map withthe global feature strings of the canonical map.

It should be appreciated that an operation of an XR device may notperform all of the Acts (300, 302, 304). For example, if a universe ofcanonical map is relatively small (e.g., 500 maps), an XR deviceattempting to localize may filter the universe of canonical maps basedon Wi-Fi fingerprints (e.g., Act 302) and Key Frame (e.g., Act 304), butomit filtering based on areas (e.g., Act 300). Moreover, it is notnecessary that maps in their entireties be compared. In someembodiments, for example, a comparison of two maps may result inidentifying common persistent points, such as persistent poses or PCFsthat appear in both the new map the selected map from the universe ofmaps. In that case, descriptors may be associated with persistentpoints, and those descriptors may be compared.

FIG. 29 is flow chart illustrating a method 900 of selecting one or moreranked environment maps, according to some embodiments. In theillustrated embodiment, the ranking is performed for a user's AR devicethat is creating a tracking map. Accordingly, the tracking map isavailable for use in ranking environment maps. In embodiments in whichthe tracking map is not available, some or all of portions of theselection and ranking of environment maps that do not expressly rely onthe tracking map may be used.

The method 900 may start at Act 902, where a set of maps from a databaseof environment maps (which may be formatted as canonical maps) that arein the neighborhood of the location where the tracking map was formedmay be accessed and then filtered for ranking. Additionally, at Act 902,at least one area attribute for the area in which the user's AR deviceis operating is determined. In scenarios in which the user's AR deviceis constructing a tracking map, the area attributes may correspond tothe area over which the tracking map was created. As a specific example,the area attributes may be computed based on received signals fromaccess points to computer networks while the AR device was computing thetracking map.

FIG. 30 depicts an exemplary map rank portion 806 of the AR system 800,according to some embodiments. The map rank portion 806 may be executingin a cloud computing environment, as it may include portions executingon AR devices and portions executing on a remote computing system suchas a cloud. The map rank portion 806 may be configured to perform atleast a portion of the method 900.

FIG. 31A depicts an example of area attributes AA1-AA8 of a tracking map(TM) 1102 and environment maps CM1-CM4 in a database, according to someembodiments. As illustrated, an environment map may be associated tomultiple area attributes. The area attributes AA1-AA8 may includeparameters of wireless networks detected by the AR device computing thetracking map 1102, for example, basic service set identifiers (BSSIDs)of networks to which the AR device are connected and/or the strength ofthe received signals of the access points to the wireless networksthrough, for example, a network tower 1104. The parameters of thewireless networks may comply with protocols including Wi-Fi and 5G NR.In the example illustrated in FIG. 32, the area attributes are afingerprint of the area in which the user AR device collected sensordata to form the tracking map.

FIG. 31B depicts an example of the determined geographic location 1106of the tracking map 1102, according to some embodiments. In theillustrated example, the determined geographic location 1106 includes acentroid point 1110 and an area 1108 circling around the centroid point.It should be appreciated that the determination of a geographic locationof the present application is not limited to the illustrated format. Adetermined geographic location may have any suitable formats including,for example, different area shapes. In this example, the geographiclocation is determined from area attributes using a database relatingarea attributes to geographic locations. Databases are commerciallyavailable, for example, databases that relate Wi-Fi fingerprints tolocations expressed as latitude and longitude and may be used for thisoperation.

In the embodiment of FIG. 29, a map database, containing environmentmaps may also include location data for those maps, including latitudeand longitude covered by the maps. Processing at Act 902 may entailselecting from that database a set of environment maps that covers thesame latitude and longitude determined for the area attributes of thetracking map.

Act 904 is a first filtering of the set of environment maps accessed inAct 902. In Act 902, environment maps are retained in the set based onproximity to the geolocation of the tracking map. This filtering stepmay be performed by comparing the latitude and longitude associated withthe tracking map and the environment maps in the set.

FIG. 32 depicts an example of Act 904, according to some embodiments.Each area attribute may have a corresponding geographic location 1202.The set of environment maps may include the environment maps with atleast one area attribute that has a geographic location overlapping withthe determined geographic location of the tracking map. In theillustrated example, the set of identified environment maps includesenvironment maps CM1, CM2, and CM4, each of which has at least one areaattribute that has a geographic location overlapping with the determinedgeographic location of the tracking map 1102. The environment map CM3associated with the area attribute AA6 is not included in the setbecause it is outside the determined geographic location of the trackingmap.

Other filtering steps may also be performed on the set of environmentmaps to reduce/rank the number of environment maps in the set that isultimately processed (such as for map merge or stitch or to providepassable world information to a user device). The method 900 may includefiltering (Act 906) the set of environment maps based on similarity ofone or more identifiers of network access points associated with thetracking map and the environment maps of the set of environment maps.During the formation of a map, a device collecting sensor data togenerate the map may be connected to a network through a network accesspoint, such as through Wi-Fi or similar wireless communication protocol.The access points may be identified by BSSID. The user device mayconnect to multiple different access points as it moves through an areacollecting data to form a map. Likewise, when multiple devices supplyinformation to form a map, the devices may have connected throughdifferent access points, so there may be multiple access points used informing the map for that reason too. Accordingly, there may be multipleaccess points associated with a map, and the set of access points may bean indication of location of the map. Strength of signals from an accesspoint, which may be reflected as an RSSI value, may provide furthergeographic information. In some embodiments, a list of BSSID and RSSIvalues may form the area attribute for a map.

In some embodiments, filtering the set of environment maps based onsimilarity of the one or more identifiers of the network access pointsmay include retaining in the set of environment maps environment mapswith the highest Jaccard similarity to the at least one area attributeof the tracking map based on the one or more identifiers of networkaccess points. FIG. 33 depicts an example of Act 906, according to someembodiments. In the illustrated example, a network identifier associatedwith the area attribute AA7 may be determined as the identifier for thetracking map 1102. The set of environment maps after Act 906 includesenvironment map CM2, which may have area attributes within higherJaccard similarity to AA7, and environment map CM4, which also includethe area attributes AA7. The environment map CM1 is not included in theset because it has the lowest Jaccard similarity to AA7.

Processing at Acts 902-906 may be performed based on metadata associatedwith maps and without actually accessing the content of the maps storedin a map database. Other processing may involve accessing the content ofthe maps. Act 908 indicates accessing the environment maps remaining inthe subset after filtering based on metadata. It should be appreciatedthat this act may be performed either earlier or later in the process,if subsequent operations can be performed with accessed content.

The method 900 may include filtering (Act 910) the set of environmentmaps based on similarity of metrics representing content of the trackingmap and the environment maps of the set of environment maps. The metricsrepresenting content of the tracking map and the environment maps mayinclude vectors of values computed from the contents of the maps. Forexample, the Deep Key Frame descriptor, as described above, computed forone or more key frames used in forming a map may provide a metric forcomparison of maps, or portions of maps. The metrics may be computedfrom the maps retrieved at Act 908 or may be pre-computed and stored asmetadata associated with those maps. In some embodiments, filtering theset of environment maps based on similarity of metrics representingcontent of the tracking map and the environment maps of the set ofenvironment maps, may include retaining in the set of environment mapsenvironment maps with the smallest vector distance between a vector ofcharacteristics of the tracking map and vectors representing environmentmaps in the set of environment maps.

The method 900 may include further filtering (Act 912) the set ofenvironment maps based on degree of match between a portion of thetracking map and portions of the environment maps of the set ofenvironment maps. The degree of match may be determined as a part of alocalization process. As a non-limiting example, localization may beperformed by identifying critical points in the tracking map and theenvironment map that are sufficiently similar as they could representthe same portion of the physical world. In some embodiments, thecritical points may be features, feature descriptors, key frames/keyrigs, persistent poses, and/or PCFs. The set of critical points in thetracking map might then be aligned to produce a best fit with the set ofcritical points in the environment map. A mean square distance betweenthe corresponding critical points might be computed and, if below athreshold for a particular region of the tracking map, used as anindication that the tracking map and the environment map represent thesame region of the physical world.

In some embodiments, filtering the set of environment maps based ondegree of match between a portion of the tracking map and portions ofthe environment maps of the set of environment maps may includecomputing a volume of a physical world represented by the tracking mapthat is also represented in an environment map of the set of environmentmaps, and retaining in the set of environment maps environment maps witha larger computed volume than environment maps filtered out of the set.FIG. 34 depicts an example of Act 912, according to some embodiments. Inthe illustrated example, the set of environment maps after Act 912includes environment map CM4, which has an area 1402 matched with anarea of the tracking map 1102. The environment map CM1 is not includedin the set because it has no area matched with an area of the trackingmap 1102.

In some embodiments, the set of environment maps may be filtered in theorder of Act 906, Act 910, and Act 912. In some embodiments, the set ofenvironment maps may be filtered based on Act 906, Act 910, and Act 912,which may be performed in an order based on processing required toperform the filtering, from lowest to highest. The method 900 mayinclude loading (Act 914) the set of environment maps and data.

In the illustrated example, a user database stores area identitiesindicating areas that AR devices were used in. The area identities maybe area attributes, which may include parameters of wireless networksdetected by the AR devices when in use. A map database may storemultiple environment maps constructed from data supplied by the ARdevices and associated metadata. The associated metadata may includearea identities derived from the area identities of the AR devices thatsupplied data from which the environment maps were constructed. An ARdevice may send a message to a PW module indicating a new tracking mapis created or being created. The PW module may compute area identifiersfor the AR device and updates the user database based on the receivedparameters and/or the computed area identifiers. The PW module may alsodetermine area identifiers associated with the AR device requesting theenvironment maps, identify sets of environment maps from the mapdatabase based on the area identifiers, filter the sets of environmentmaps, and transmit the filtered sets of environment maps to the ARdevices. In some embodiments, the PW module may filter the sets ofenvironment maps based on one or more criteria including, for example, ageographic location of the tracking map, similarity of one or moreidentifiers of network access points associated with the tracking mapand the environment maps of the set of environment maps, similarity ofmetrics representing contents of the tracking map and the environmentmaps of the set of environment maps, and degree of match between aportion of the tracking map and portions of the environment maps of theset of environment maps.

FIGS. 35 and 36 are schematic diagrams illustrating an XR systemconfigured to rank and merge or stitch a plurality of environment maps,according to some embodiments. In some embodiments, a passable world(PW) may determine when to trigger ranking and/or merging or stitchingthe maps. In some embodiments, determining a map to be used may be basedat least partly on deep key frames described above in relation to FIGS.21-25, according to some embodiments.

FIG. 37 is a block diagram illustrating a method 3700 of creatingenvironment maps of a physical world, according to some embodiments. Themethod 3700 may start from localizing (Act 3702) a tracking map capturedby an XR device worn by a user to a group of canonical maps (e.g.,canonical maps selected by the method of FIG. 28 and/or the method 900of FIG. 29). The Act 3702 may include localizing keyrigs of the trackingmap into the group of canonical maps. The localization result of eachkeyrig may include the keyrig's localized pose and a set of 2D-to-3Dfeature correspondences.

In some embodiments, the method 3700 may include splitting (Act 3704) atracking map into connected components, which may enable merging orstitching maps robustly by merging or stitching connected pieces. Eachconnected component may include keyrigs that are within a predetermineddistance. The method 3700 may include merging or stitching (Act 3706)the connected components that are larger than a predetermined thresholdinto one or more canonical maps, and removing the merged or stitchedconnected components from the tracking map.

In some embodiments, the method 3700 may include merging or stitching(Act 3708) canonical maps of the group that are merged or stitched withthe same connected components of the tracking map. In some embodiments,the method 3700 may include promoting (Act 3710) the remaining connectedcomponents of the tracking map that has not been merged or stitched withany canonical maps to be a canonical map. In some embodiments, themethod 3700 may include merging or stitching (Act 3712) persistent posesand/or PCFs of the tracking maps and the canonical maps that are mergedor stitched with at least one connected component of the tracking map.In some embodiments, the method 3700 may include finalizing (Act 3714)the canonical maps by, for example, fusing map points and pruningredundant keyrigs.

FIGS. 38A and 38B illustrate an environment map 3800 created by updatinga canonical map 700, which may be promoted from the tracking map 700(FIG. 7) with a new tracking map, according to some embodiments. Asillustrated and described with respect to FIG. 7, the canonical map 700may provide a floor plan 706 of reconstructed physical objects in acorresponding physical world, represented by points 702. In someembodiments, a map point 702 may represent a feature of a physicalobject that may include multiple features. A new tracking map may becaptured about the physical world and uploaded to a cloud to merge orstitch with the map 700. The new tracking map may include map points3802, and keyrigs 3804, 3806. In the illustrated example, keyrigs 3804represent keyrigs that are successfully localized to the canonical mapby, for example, establishing a correspondence with a keyrig 704 of themap 700 (as illustrated in FIG. 38B). On the other hand, keyrigs 3806represent keyrigs that have not been localized to the map 700. Keyrigs3806 may be promoted to a separate canonical map in some embodiments.

FIGS. 39A to 39F are schematic diagrams illustrating an example of acloud-based persistent coordinate system providing a shared experiencefor users in the same physical space. FIG. 39A shows that a canonicalmap 4814, for example, from a cloud, is received by the XR devices wornby the users 4802A and 4802B of FIGS. 20A-20C. The canonical map 4814may have a canonical coordinate frame 4806C. The canonical map 4814 mayhave a PCF 4810C with a plurality of associated PPs (e.g., 4818A, 4818Bin FIG. 39C).

FIG. 39B shows that the XR devices established relationships betweentheir respective world coordinate system 4806A, 4806B with the canonicalcoordinate frame 4806C. This may be done, for example, by localizing tothe canonical map 4814 on the respective devices. Localizing thetracking map to the canonical map may result, for each device, atransformation between its local world coordinate system and thecoordinate system of the canonical map.

FIG. 39C shows that, as a result of localization, a transformation canbe computed (e.g., transformation 4816A, 4816B) between a local PCF(e.g., PCFs 4810A, 4810B) on the respective device to a respectivepersistent pose (e.g., PPs 4818A, 4818B) on the canonical map. Withthese transformations, each device may use its local PCFs, which can bedetected locally on the device by processing images detected withsensors on the device, to determine where with respect to the localdevice to display virtual content attached to the PPs 4818A, 4818B orother persistent points of the canonical map. Such an approach mayaccurately position virtual content with respect to each user and mayenable each user to have the same experience of the virtual content inthe physical space.

FIG. 39D shows a persistent pose snapshot from the canonical map to thelocal tracking maps. As can be seen, the local tracking maps areconnected to one another via the persistent poses. FIG. 39E shows thatthe PCF 4810A on the device worn by the user 4802A is accessible in thedevice worn by the user 4802B through PPs 4818A. FIG. 39F shows that thetracking maps 4804A, 4804B and the canonical 4814 may merge or stitch.In some embodiments, some PCFs may be removed as a result of merging orstitching. In the illustrated example, the merged or stitched mapincludes the PCF 4810C of the canonical map 4814 but not the PCFs 4810A,4810B of the tracking maps 4804A, 4804B. The PPs previously associatedwith the PCFs 4810A, 4810B may be associated with the PCF 4810C afterthe maps merge or stitch.

EXAMPLES

FIGS. 40 and 41 illustrate an example of using a tracking map by thefirst XR device 12.1 of FIG. 9. FIG. 40 is a two-dimensionalrepresentation of a three-dimensional first local tracking map (Map 1),which may be generated by the first XR device of FIG. 9, according tosome embodiments. FIG. 41 is a block diagram illustrating uploading Map1 from the first XR device to the server of FIG. 9, according to someembodiments.

FIG. 40 illustrates Map 1 and virtual content (Content123 andContent456) on the first XR device 12.1. Map 1 has an origin (Origin 1).Map 1 includes a number of PCFs (PCF a to PCF d). From the perspectiveof the first XR device 12.1, PCF a, by way of example, is located at theorigin of Map 1 and has X, Y, and Z coordinates of (0,0,0) and PCF b hasX, Y, and Z coordinates (−1,0,0). Content123 is associated with PCF a.In the present example, Content123 has an X, Y, and Z relationshiprelative to PCF a of (1,0,0). Content456 has a relationship relative toPCF b. In the present example, Content456 has an X, Y, and Zrelationship of (1,0,0) relative to PCF b.

In FIG. 41, the first XR device 12.1 uploads Map 1 to the server 20. Inthis example, as the server stores no canonical map for the same regionof the physical world represented by the tracking map, and the trackingmap is stored as an initial canonical map. The server 20 now has acanonical map based on Map 1. The first XR device 12.1 has a canonicalmap that is empty at this stage. The server 20, for purposes ofdiscussion, and in some embodiments, includes no other maps other thanMap 1. No maps are stored on the second XR device 12.2.

The first XR device 12.1 also transmits its Wi-Fi signature data to theserver 20. The server 20 may use the Wi-Fi signature data to determine arough location of the first XR device 12.1 based on intelligencegathered from other devices that have, in the past, connected to theserver 20 or other servers together with the GPS locations of such otherdevices that have been recorded. The first XR device 12.1 may now endthe first session (See FIG. 8) and may disconnect from the server 20.

FIG. 42 is a schematic diagram illustrating the XR system of FIG. 16,showing the second user 14.2 has initiated a second session using asecond XR device of the XR system after the first user 14.1 hasterminated a first session, according to some embodiments. FIG. 43A is ablock diagram showing the initiation of a second session by a seconduser 14.2. The first user 14.1 is shown in phantom lines because thefirst session by the first user 14.1 has ended. The second XR device12.2 begins to record objects. Various systems with varying degrees ofgranulation may be used by the server 20 to determine that the secondsession by the second XR device 12.2 is in the same vicinity of thefirst session by the first XR device 12.1. For example, Wi-Fi signaturedata, global positioning system (GPS) positioning data, GPS data basedon Wi-Fi signature data, or any other data that indicates a location maybe included in the first and second XR devices 12.1 and 12.2 to recordtheir locations. Alternatively, the PCFs that are identified by thesecond XR device 12.2 may show a similarity to the PCFs of Map 1.

As shown in FIG. 43B, the second XR device boots up and begins tocollect data, such as images 1110 from one or more cameras 44, 46. Asshown in FIG. 14, in some embodiments, an XR device (e.g., the second XRdevice 12.2) may collect one or more images 1110 and perform imageprocessing to extract one or more features/interest points 1120. Eachfeature may be converted to a descriptor 1130. In some embodiments, thedescriptors 1130 may be used to describe a key frame 1140, which mayhave the position and direction of the associated image attached. One ormore key frames 1140 may correspond to a single persistent pose 1150,which may be automatically generated after a threshold distance from theprevious persistent pose 1150, e.g., 3 meters. One or more persistentposes 1150 may correspond to a single PCF 1160, which may beautomatically generated after a pre-determined distance, e.g., every 5meters. Over time as the user continues to move around the user'senvironment, and the XR device continues to collect more data, such asimages 1110, additional PCFs (e.g., PCF 3 and PCF 4, 5) may be created.One or more applications 1180 may run on the XR device and providevirtual content 1170 to the XR device for presentation to the user. Thevirtual content may have an associated content coordinate frame whichmay be placed relative to one or more PCFs. As shown in FIG. 43B, thesecond XR device 12.2 creates three PCFs. In some embodiments, thesecond XR device 12.2 may try to localize into one or more canonicalmaps stored on the server 20.

In some embodiments, as shown in FIG. 43C, the second XR device 12.2 maydownload the canonical map 120 from the server 20. Map 1 on the secondXR device 12.2 includes PCFs a to d and Origin 1. In some embodiments,the server 20 may have multiple canonical maps for various locations andmay determine that the second XR device 12.2 is in the same vicinity asthe vicinity of the first XR device 12.1 during the first session andsends the second XR device 12.2 the canonical map for that vicinity.

FIG. 44 shows the second XR device 12.2 beginning to identify PCFs forpurposes of generating Map 2. The second XR device 12.2 has onlyidentified a single PCF, namely PCF 1,2. The X, Y, and Z coordinates ofPCF 1,2 for the second XR device 12.2 may be (1,1,1). Map 2 has its ownorigin (Origin 2), which may be based on the headpose of device 2 atdevice start-up for the current headpose session. In some embodiments,the second XR device 12.2 may immediately attempt to localize Map 2 tothe canonical map. In some embodiments, Map 2 may not be able tolocalize into Canonical Map (Map 1) (i.e., localization may fail)because the system does not recognize any or enough overlap between thetwo maps. Localization may be performed by identifying a portion of thephysical world represented in a first map that is also represented in asecond map, and computing a transformation between the first map and thesecond map required to align those portions. In some embodiments, thesystem may localize based on PCF comparison between the local andcanonical maps. In some embodiments, the system may localize based onpersistent pose comparison between the local and canonical maps. In someembodiments, the system may localize based on key frame comparisonbetween the local and canonical maps.

FIG. 45 shows Map 2 after the second XR device 12.2 has identifiedfurther PCFs (PCF 1,2, PCF 3, PCF 4,5) of Map 2. The second XR device12.2 again attempts to localize Map 2 to the canonical map. Because Map2 has expanded to overlap with at least a portion of the Canonical Map,the localization attempt will succeed. In some embodiments, the overlapbetween the local tracking map, Map 2, and the Canonical Map may berepresented by PCFs, persistent poses, key frames, or any other suitableintermediate or derivative construct.

Furthermore, the second XR device 12.2 has associated Content123 andContent456 to PCFs 1,2 and PCF 3 of Map 2. Content123 has X, Y, and Zcoordinates relative to PCF 1,2 of (1,0,0). Similarly, the X, Y, and Zcoordinates of Content456 relative to PCF 3 in Map 2 are (1,0,0).

FIGS. 46A and 46B illustrate a successful localization of Map 2 to thecanonical map. Localization may be based on matching features in one mapto the other. With an appropriate transformation, here involving bothtranslation and rotation of one map with respect to the other, theoverlapping area/volume/section of the maps 1410 represent the commonparts to Map 1 and the canonical map. Since Map 2 created PCFs 3 and 4,5before localizing, and the Canonical map created PCFs a and c before Map2 was created, different PCFs were created to represent the same volumein real space (e.g., in different maps).

As shown in FIG. 47, the second XR device 12.2 expands Map 2 to includePCFs a-d from the Canonical Map. The inclusion of PCFs a-d representsthe localization of Map 2 to the Canonical Map. In some embodiments, theXR system may perform an optimization step to remove duplicate PCFs fromoverlapping areas, such as the PCFs in 1410, PCF 3 and PCF 4,5. AfterMap 2 localizes, the placement of virtual content, such as Content456and Content123 will be relative to the closest updated PCFs in theupdated Map 2. The virtual content appears in the same real-worldlocation relative to the user, despite the changed PCF attachment forthe content, and despite the updated PCFs for Map 2.

As shown in FIG. 48, the second XR device 12.2 continues to expand Map 2as further PCFs (e.g., PCFs e, f, g, and h) are identified by the secondXR device 12.2, for example as the user walks around the real world. Itcan also be noted that Map 1 has not expanded in FIGS. 47 and 48.

Referring to FIG. 49, the second XR device 12.2 uploads Map 2 to theserver 20. The server 20 stores Map 2 together with the canonical map.In some embodiments, Map 2 may upload to the server 20 when the sessionends for the second XR device 12.2.

The canonical map within the server 20 now includes PCF i which is notincluded in Map 1 on the first XR device 12.1. The canonical map on theserver 20 may have expanded to include PCF i when a third XR device (notshown) uploaded a map to the server 20 and such a map included PCF i.

In FIG. 50, the server 20 merges or stitches Map 2 with the canonicalmap to form a new canonical map. The server 20 determines that PCFs a tod are common to the canonical map and Map 2. The server expands thecanonical map to include PCFs e to h and PCF 1,2 from Map 2 to form anew canonical map. The canonical maps on the first and second XR devices12.1 and 12.2 are based on Map 1 and are outdated.

In FIG. 51, the server 20 transmits the new canonical map to the firstand second XR devices 12.1 and 12.2. In some embodiments, this may occurwhen the first XR device 12.1 and second device 12.2 try to localizeduring a different or new or subsequent session. The first and second XRdevices 12.1 and 12.2 proceed as described above to localize theirrespective local maps (Map1 and Map 2 respectively) to the new canonicalmap.

As shown in FIG. 52, the head coordinate frame 96 or “headpose” isrelated to the PCFs in Map 2. In some embodiments, the origin of themap, Origin 2, is based on the headpose of second XR device 12.2 at thestart of the session. As PCFs are created during the session, the PCFsare placed relative to the world coordinate frame, Origin 2. The PCFs ofMap 2 serve as a persistent coordinate frames relative to a canonicalcoordinate frame, where the world coordinate frame may be a previoussession's world coordinate frame (e.g., Map 1's Origin 1 in FIG. 40).These coordinate frames are related by the same transformation used tolocalize Map 2 to the canonical map, as discussed above in connectionwith FIG. 46B.

The transformation from the world coordinate frame to the headcoordinate frame 96 has been previously discussed with reference to FIG.9. The head coordinate frame 96 shown in FIG. 52 only has two orthogonalaxes that are in a particular coordinate position relative to the PCFsof Map 2, and at particular angles relative to Map 2. It should howeverbe understood that the head coordinate frame 96 is in athree-dimensional location relative to the PCFs of Map 2 and has threeorthogonal axes within three-dimensional space.

In FIG. 53, the head coordinate frame 96 has moved relative to the PCFsof Map 2. The head coordinate frame 96 has moved because the second user14.2 has moved their head. The user can move their head in six degreesof freedom (6dof). The head coordinate frame 96 can thus move in 6dof,namely in three-dimensions from its previous location in FIG. 52 andabout three orthogonal axes relative to the PCFs of Map 2. The headcoordinate frame 96 is adjusted when the real object detection camera 44and inertial measurement unit 48 in FIG. 9 respectively detect realobjects and motion of the head unit 22. More information regardingheadpose tracking is disclosed in U.S. patent application Ser. No.16/221,065 entitled “Enhanced Pose Determination for Display Device” andis hereby incorporated by reference in its entirety.

FIG. 54 shows that sound may be associated with one or more PCFs. A usermay, for example, wear headphones or earphones with stereoscopic sound.The location of sound through headphones can be simulated usingconventional techniques. The location of sound may be located in astationary position so that, when the user rotates their head to theleft, the location of sound rotates to the right so that the userperceives the sound coming from the same location in the real world. Inthe present example, location of sound is represented by Sound123 andSound456. For purposes of discussion, FIG. 54 is similar to FIG. 48 inits analysis. When the first and second users 14.1 and 14.2 are locatedin the same room at the same or different times, they perceive Sound123and Sound456 coming from the same locations within the real world.

FIGS. 55 and 56 illustrate a further implementation of the technologydescribed above. The first user 14.1 has initiated a first session asdescribed with reference to FIG. 8. As shown in FIG. 55, the first user14.1 has terminated the first session as indicated by the phantom lines.At the end of the first session, the first XR device 12.1 uploaded Map 1to the server 20. The first user 14.1 has now initiated a second sessionat a later time than the first session. The first XR device 12.1 doesnot download Map 1 from the server 20 because Map 1 is already stored onthe first XR device 12.1. If Map 1 is lost, then the first XR device12.1 downloads Map 1 from the server 20. The first XR device 12.1 thenproceeds to build PCFs for Map 2, localizes to Map 1, and furtherdevelops a canonical map as described above. Map 2 of the first XRdevice 12.1 is then used for relating local content, a head coordinateframe, local sound, etc. as described above.

Referring to FIGS. 57 and 58, it may also be possible that more than oneuser interacts with the server in the same session. In the presentexample, the first user 14.1 and the second user 14.2 are joined by athird user 14.3 with a third XR device 12.3. Each XR device 12.1, 12.2,and 12.3 begins to generate its own map, namely Map 1, Map 2, and Map 3,respectively. As the XR devices 12.1, 12.2, and 12.3 continue to developMaps 1, 2, and 3, the maps are incrementally uploaded to the server 20.The server 20 merges or stitches Maps 1, 2, and 3 to form a canonicalmap. The canonical map is then transmitted from the server 20 to eachone of the XR devices 12.1, 12.2 and 12.3.

FIG. 59 illustrates aspects of a viewing method to recover and/or resetheadpose, according to some embodiments. In the illustrated example, atAct 1400, the viewing device is powered on. At Act 1410, in response tobeing powered on, a new session is initiated. In some embodiments, a newsession may include establishing headpose. One or more capture deviceson a head-mounted frame secured to a head of a user capture surfaces ofan environment by first capturing images of the environment and thendetermining the surfaces from the images. In some embodiments, surfacedata may be combined with a data from a gravitational sensor toestablish headpose. Other suitable methods of establishing headpose maybe used.

At Act 1420, a processor of the viewing device enters a routine fortracking of headpose. The capture devices continue to capture surfacesof the environment as the user moves their head to determine anorientation of the head-mounted frame relative to the surfaces.

At Act 1430, the processor determines whether headpose has been lost.Headpose may become lost due to “edge” cases, such as too manyreflective surfaces, low light, blank walls, being outdoor, etc. thatmay result in low feature acquisition, or because of dynamic cases suchas a crowd that moves and forms part of the map. The routine at 1430allows for a certain amount of time, for example 10 seconds, to pass toallow enough time to determine whether headpose has been lost. Ifheadpose has not been lost, then the processor returns to 1420 and againenters tracking of headpose.

If headpose has been lost at Act 1430, the processor enters a routine at1440 to recover headpose. If headpose is lost due to low light, then amessage such as the following message is displayed to the user through adisplay of the viewing device:

THE SYSTEM IS DETECTING A LOW LIGHT CONDITION. PLEASE MOVE TO AN AREAWHERE THERE IS MORE LIGHT.

The system will continue to monitor whether there is sufficient lightavailable and whether headpose can be recovered. The system mayalternatively determine that low texture of surfaces is causing headposeto be lost, in which case the user is given the following prompt in thedisplay as a suggestion to improve capturing of surfaces:

THE SYSTEM CANNOT DETECT ENOUGH SURFACES WITH FINE TEXTURES. PLEASE MOVETO AN AREA WHERE THE SURFACES ARE LESS ROUGH IN TEXTURE AND MORE REFINEDIN TEXTURE.

At Act 1450, the processor enters a routine to determine whetherheadpose recovery has failed. If headpose recovery has not failed (i.e.,headpose recovery has succeeded), then the processor returns to Act 1420by again entering tracking of headpose. If headpose recovery has failed,the processor returns to Act 1410 to establish a new session. As part ofthe new session, all cached data is invalidated, whereafter headpose isestablished anew. Any suitable method of head tracking may be used incombination with the process described in FIG. 59. U.S. patentapplication Ser. No. 16/221,065 describes head tracking and is herebyincorporated by reference in its entirety.

Remote Localization

Various embodiments may utilize remote resources to facilitatepersistent and consistent cross reality experiences between individualand/or groups of users. The inventors have recognized and appreciatedthat the benefits of operation of an XR device with canonical maps asdescribed herein can be achieved without downloading a set of canonicalmaps, such as is illustrated in FIG. 30. The benefit, for example, maybe achieved by sending feature and pose information to a remote servicethat maintains a set of canonical maps. A device seeking to use acanonical map to position virtual content in locations specifiedrelative to the canonical map may receive from the remote service one ormore transformations between the features and the canonical maps. Thosetransformations may be used on the device, which maintains informationabout the positions of those features in the physical world, to positionvirtual content in locations specified with respect to canonical map orto otherwise identify locations in the physical world that are specifiedwith respect to the canonical map.

In some embodiments, spatial information is captured by an XR device andcommunicated to a remote service, such as a cloud-based service, whichuses the spatial information to localize the XR device to a canonicalmap used by applications or other components of an XR system to specifythe location of virtual content with respect to the physical world. Oncelocalized, transforms that link a tracking map maintained by the deviceto the canonical map can be communicated to the device. The transformsmay be used, in conjunction with the tracking map, to determine aposition in which to render virtual content specified with respect tothe canonical map, or otherwise identify locations in the physical worldthat are specified with respect to the canonical map.

The inventors have realized that the data needed to be exchanged betweena device and a remote localization service can be quite small relativeto communicating map data, as might occur when a device communicates atracking map to a remote service and receives from that service a set ofcanonical maps for device-based localization). In some embodiments,performing localization functions on cloud resources requires only smallamount of information to be transmitted from the device to the remoteservice. It is not a requirement, for example, that a full tracking mapbe communicated to the remote service to perform localization. In someembodiments, features and pose information, such as might be stored inconnection with a persistent pose, as described above, might betransmitted to the remote server. In embodiments in which features arerepresented by descriptors, as described above, the information uploadedmay be even smaller.

The results returned to the device from the localization service may beone or more transformations that relate the uploaded features toportions of a matching canonical map. Those transformations may be usedwithin the XR system, in conjunction with its tracking map, foridentifying locations of virtual content or otherwise identifyinglocations in the physical world. In embodiments in which persistentspatial information, such as PCFs as described above, are used tospecify locations with respect to a canonical map, the localizationservice may download to the device transformations between the featuresand one or more PCFs after a successful localization.

As a result, network bandwidth consumed by communications between an XRdevice and a remote service for performing localization may be low. Thesystem may therefore support frequent localization, enabling each deviceinteracting with the system to quickly obtain information forpositioning virtual content or performing other location-basedfunctions. As a device moves within the physical environment, it mayrepeat requests for updated localization information. Additionally, adevice may frequently obtain updates to the localization information,such as when the canonical maps change, such as through merging orstitching of additional tracking maps to expand the map or increasetheir accuracy.

Further, uploading features and downloading transformations can enhanceprivacy in an XR system that shares map information among multiple usersby increasing the difficulty of obtaining maps by spoofing. Anunauthorized user, for example, may be thwarted from obtaining a mapfrom the system by sending a fake request for a canonical maprepresenting a portion of the physical world in which that unauthorizeduser is not located. An unauthorized user would be unlikely to haveaccess to the features in the region of the physical world for which itis requesting map information if not physically present in that region.In embodiments in which feature information is formatted as featuredescriptions, the difficulty in spoofing feature information in arequest for map information would be compounded. Further, when thesystem returns a transformation intended to be applied to a tracking mapof a device operating in the region about which location information isrequested, the information returned by the system is likely to be oflittle or no use to an imposter.

According to one embodiment, a localization service is implemented as acloud based micro-service. In some examples, implementing a cloud-basedlocalization service can help save device compute resources and mayenable computations required for localization to be performed with verylow latency. Those operations can be supported by nearly infinitecompute power or other computing resources available by provisioningadditional cloud resources, ensuring scalability of the XR system tosupport numerous devices. In one example, many canonical maps can bemaintained in memory for nearly instant access, or alternatively storedin high availability devices reducing system latency.

Further, performing localization for multiple devices in a cloud servicemay enable refinements to the process. Localization telemetry andstatistics can provide information on which canonical maps to have inactive memory and/or high availability storage. Statistics for multipledevices may be used, for example, to identify most frequently accessedcanonical maps.

Additional accuracy may also be achieved as a result of processing in acloud environment or other remote environment with substantialprocessing resources relative to a remote device. For example,localization can be made on higher density canonical maps in the cloudrelative to processing performed on local devices. Maps may be stored inthe cloud, for example, with more PCFs or a greater density of featuredescriptors per PCF, increasing the accuracy of a match between a set offeatures from a device and a canonical map.

FIG. 61 is a schematic diagram of an XR system 6100. The user devicesthat display cross reality content during user sessions can come in avariety of forms. For example, a user device can be a wearable XR device(e.g., 6102) or a handheld mobile device (e.g., 6104). As discussedabove, these devices can be configured with software, such asapplications or other components, and/or hardwired to generate localposition information (e.g., a tracking map) that can be used to rendervirtual content on their respective displays.

Virtual content positioning information may be specified with respect toglobal location information, which may be formatted as a canonical mapcontaining one or more PCFs, for example. According to some embodiments,for example the embodiment shown in FIG. 61, the system 6100 isconfigured with cloud-based services that support the functioning anddisplay of the virtual content on the user device.

In one example, localization functions are provided as a cloud-basedservice 6106, which may be a micro-service. Cloud-based service 6106 maybe implemented on any of multiple computing devices, from whichcomputing resources may be allocated to one or more services executingin the cloud. Those computing devices may be interconnected with eachother and accessibly to devices, such as a wearable XR device 6102 andhand-held device 6104. Such connections may be provided over one or morenetworks.

In some embodiments, the cloud-based service 6106 is configured toaccept descriptor information from respective user devices and“localize” the device to a matching canonical map or maps. For example,the cloud-based localization service matches descriptor informationreceived to descriptor information for respective canonical map(s). Thecanonical maps may be created using techniques as described above thatcreate canonical maps by merging or stitching maps provided by one ormore devices that have image sensors or other sensors that acquireinformation about a physical world. However, it is not a requirementthat the canonical maps be created by the devices that access them, assuch maps may be created by a map developer, for example, who maypublish the maps by making them available to localization service 6106.

According to some embodiments, the cloud service handles canonical mapidentification, and may include operations to filter a repository ofcanonical maps to a set of potential matches. Filtering may be performedas illustrated in FIG. 29, or by using any subset of the filter criteriaand other filter criteria instead of or in addition to the filtercriteria shown in FIG. 29. In one embodiment, geographic data can beused to limit a search for matching canonical map to maps representingareas proximate to the device requesting localization. For example, areaattributes such as Wi-Fi signal data, Wi-Fi fingerprint information, GPSdata, and/or other device location information can be used as a coarsefilter on stored canonical maps, and thereby limit analysis ofdescriptors to canonical maps known or likely to be in proximity to theuser device. Similarly, location history of each device may bemaintained by the cloud service such that canonical maps in the vicinityof the device's last location are preferentially searched. In someexamples, filtering can include the functions discussed above withrespect to FIGS. 31B, 32, 33, and 34.

FIG. 62 is an example process flow that can be executed by a device touse a cloud-based service to localize the device's position withcanonical map(s) and receive transform information specifying one ormore transformations between the device local coordinate system and thecoordinate system of a canonical map. Various embodiments and examplesmay describe the one or more transforms as specifying transforms from afirst coordinate frame to a second coordinate frame. Other embodimentsinclude transforms from the second coordinate frame to the firstcoordinate frame. In yet other embodiments, the transforms enabletransition from one coordinate frame to another, the resultingcoordinate frames depend only on the desired coordinate frame output(including, for example, the coordinate frame in which to displaycontent). In yet further embodiments, the coordinate system transformsmay enable determination of a first coordinate frame from the secondcoordinate frame and the second coordinate frame from the firstcoordinate frame.

According to some embodiments, information reflecting a transform foreach persistent pose defined with respect to the canonical map can becommunicated to device.

According to one embodiment, process 6200 can begin at 6202 with a newsession. Starting new session on the device may initiate capture ofimage information to build a tracking map for the device. Additionally,the device may send a message, registering with a server of alocalization service, prompting the server to create a session for thatdevice.

In some embodiments, starting a new session on a device optionally mayinclude sending adjustment data from the device to the localizationservice. The localization service returns to the device one or moretransforms computed based on the set of features and associated poses.If the poses of the features are adjusted based on device-specificinformation before computation of the transformation and/or thetransformations are adjusted based on device-specific information aftercomputation of the transformation, rather than perform thosecomputations on the device, the device specific information might besent to the localization service, such that the localization service mayapply the adjustments. As a specific example, sending device-specificadjustment information may include capturing calibration data forsensors and/or displays. The calibration data may be used, for example,to adjust the locations of feature points relative to a measuredlocation. Alternatively or additionally, the calibration data may beused to adjust the locations at which the display is commanded to rendervirtual content so as to appear accurately positioned for thatparticular device. This calibration data may be derived, for example,from multiple images of the same scene taken with sensors on the device.The locations of features detected in those images may be expressed as afunction of sensor location, such that multiple images yield a set ofequations that may be solved for the sensor location. The computedsensor location may be compared to a nominal position, and thecalibration data may be derived from any differences. In someembodiments, intrinsic information about the construction of the devicemay also enable calibration data to be computed for the display, in someembodiments.

In embodiments in which calibration data is generated for the sensorsand/or display, the calibration data may be applied at any point in themeasurement or display process. In some embodiments, the calibrationdata may be sent to the localization server, which may store thecalibration data in a data structure established for each device thathas registered with the localization server and is therefore in asession with the server. The localization server may apply thecalibration data to any transformations computed as part of alocalization process for the device supplying that calibration data. Thecomputational burden of using the calibration data for greater accuracyof sensed and/or displayed information is thus borne by the calibrationservice, providing a further mechanism to reduce processing burden onthe devices.

Once the new session is established, process 6200 may continue at 6204with capture of new frames of the device's environment. Each frame canbe processed to generate descriptors (including for example, DSF valuesdiscussed above) for the captured frame at 6206. These values may becomputed using some or all of the techniques described above, includingtechniques as discussed above with respect to FIGS. 14, 22 and 23. Asdiscussed, the descriptors may be computed as a mapping of the featurepoints or, in some embodiments a mapping of a patch of an image around afeature point, to a descriptor. The descriptor may have a value thatenables efficient matching between newly acquired frames/images andstored maps. Moreover, the number of features extracted from an imagemay be limited to a maximum number of features points per image, such as200 feature points per image. The feature points may be selected torepresent interest points, as described above. Accordingly, acts 6204and 6206 may be performed as part of a device process of forming atracking map or otherwise periodically collecting images of the physicalworld around the device, or may be, but need not be, separatelyperformed for localization.

Feature extraction at 6206 may include appending pose information to theextracted features at 6206. The pose information may be a pose in thedevice's local coordinate system. In some embodiments, the pose may berelative to a reference point in the tracking map, such as a persistentpose, as discussed above. Alternatively or additionally, the pose may berelative to the origin of a tracking map of the device. Such anembodiment may enable the localization service as described herein toprovide localization services for a wide range of devices, even if theydo not utilize persistent poses. Regardless, pose information may beappended to each feature or each set of features, such that thelocalization service may use the pose information for computing atransformation that can be returned to the device upon matching thefeatures to features in a stored map.

The process 6200 may continue to decision block 6207 where a decision ismade whether to request localization. One or more criteria may beapplied to determine whether to request localization. The criteria mayinclude passage of time, such that a device may request localizationafter some threshold amount of time. For example, if localization hasnot been attempted within a threshold amount of time, the process maycontinue from decision block 6207 to act 6208 where localization isrequested from the cloud. That threshold amount of time may be betweenten and thirty seconds, such as twenty-five seconds, for example.Alternatively or additionally, localization may be triggered by motionof a device. A device executing the process 6200 may track its motionusing an IMU and/or its tracking map, and initiate localization upondetection motion exceeding a threshold distance from the location wherethe device last requested localization. The threshold distance may bebetween one and ten meters, such as between three and five meters, forexample. As yet a further alternative, localization may be triggered inresponse to an event, such as when a device creates a new persistentpose or the current persistent pose for the device changes, as describedabove.

In some embodiments, decision block 6207 may be implemented such thatthe thresholds for triggering localization may be establisheddynamically. For example, in environments in which features are largelyuniform such that there may be a low confidence in matching a set ofextracted features to features of a stored map, localization may berequested more frequently to increase the chances that at least oneattempt at localization will succeed. In such a scenario, the thresholdsapplied at decision block 6207 may be decreased. Similarly, in anenvironment in which there are relatively few features, the thresholdsapplied at decision block 6207 may be decreased so as to increase thefrequency of localization attempts.

Regardless of how the localization is triggered, when triggered, theprocess 6200 may proceed to act 6208 where the device sends a request tothe localization service, including data used by the localizationservice to perform localization. In some embodiments, data from multipleimage frames may be provided for a localization attempt. Thelocalization service, for example, may not deem localization successfulunless features in multiple image frames yield consistent localizationresults. In some embodiments, process 6200 may include saving featuredescriptors and appended pose information into a buffer. The buffer may,for example, be a circular buffer, storing sets of features extractedfrom the most recently captured frames. Accordingly, the localizationrequest may be sent with a number of sets of features accumulated in thebuffer. In some settings, a buffer size is implemented to accumulate anumber of sets of data that will be more likely to yield successfullocalization. In some embodiments, a buffer size may be set toaccumulate features from two, three, four, five, six, seven, eight,nine, or ten frames, for example). Optionally, the buffer size can havea baseline setting which can be increased responsive to localizationfailures. In some examples, increasing the buffer size and correspondingnumber of sets of features transmitted reduces the likelihood thatsubsequent localization functions fail to return a result.

Regardless of how the buffer size is set, the device may transfer thecontents of the buffer to the localization service as part of alocalization request. Other information may be transmitted inconjunction with the feature points and appended pose information. Forexample, in some embodiments, geographic information may be transmitted.The geographic information may include, for example, GPS coordinates ora wireless signature associated with the devices tracking map or currentpersistent pose.

In response to the request sent at 6208, a cloud localization servicemay analyze the feature descriptors to localize the device into acanonical map or other persistent map maintained by the service. Forexample, the descriptors are matched to a set of features in a map towhich the device is localized. The cloud-based localization service mayperform localization as described above with respect to device-basedlocalization (e.g., can rely on any of the functions discussed above forlocalization including, map ranking, map filtering, location estimation,filtered map selection, examples in FIGS. 44-46, and/or discussed withrespect to a localization module, PCF and/or PP identification andmatching etc.). However, instead of communicating identified canonicalmaps to a device (e.g., in device localization), the cloud-basedlocalization service may proceed to generate transforms based on therelative orientation of feature sets sent from the device and thematching features of the canonical maps. The localization service mayreturn these transforms to the device, which may be received at block6210.

In some embodiments, the canonical maps maintained by the localizationservice may employ PCFs, as described above. In such embodiments, thefeature points of the canonical maps that match the feature points sentfrom the device may have positions specified with respect to one or morePCFs. Accordingly, the localization service may identify one or morecanonical maps and may compute a transformation between the coordinateframe represented in the poses sent with the request for localizationand the one or more PCFs. In some embodiments, identification of the oneor more canonical maps is assisted by filtering potential maps based ongeographic data for a respective device. For example, once filtered to acandidate set (e.g., by GPS coordinate, among other options) thecandidate set of canonical maps can be analyzed in detail to determinematching feature points or PCFs as described above.

The data returned to the requesting device at act 6210 may be formattedas a table of persistent pose transforms. The table can be accompaniedby one or more canonical map identifiers, indicating the canonical mapsto which the device was localized by the localization service. However,it should be appreciated that the localization information may beformatted in other ways, including as a list of transforms, withassociated PCF and/or canonical map identifiers.

Regardless of how the transforms are formatted, at act 6212 the devicemay use these transforms to compute the location at which to rendervirtual content for which a location has been specified by anapplication or other component of the XR system relative to any of thePCFs. This information may alternatively or additionally be used on thedevice to perform any location-based operation in which a location isspecified based on the PCFs.

In some scenarios, the localization service may be unable to matchfeatures sent from a device to any stored canonical map or may not beable to match a sufficient number of the sets of features communicatedwith the request for the localization service to deem a successfullocalization occurred. In such a scenario, rather than returningtransformations to the device as described above in connection with act6210, the localization service may indicate to the device thatlocalization failed. In such a scenario, the process 6200 may branch atdecision block 6209 to act 6230, where the device may take one or moreactions for failure processing. These actions may include increasing thesize of the buffer holding feature sets sent for localization. Forexample, if the localization service does not deem a successfullocalization unless three sets of features match, the buffer size may beincreased from five to six, increasing the chances that three of thetransmitted sets of features can be matched to a canonical mapmaintained by the localization service.

Alternatively or additionally, failure processing may include adjustingan operating parameter of the device to trigger more frequentlocalization attempts. The threshold time between localization attemptsand/or the threshold distance may be decreased, for example. As anotherexample, the number of feature points in each set of features may beincreased. A match between a set of features and features stored withina canonical map may be deemed to occur when a sufficient number offeatures in the set sent from the device match features of the map.Increasing the number of features sent may increase the chances of amatch. As a specific example, the initial feature set size may be 50,which may be increased to 100, 150, and then 200, on each successivelocalization failure. Upon successful match, the set size may then bereturned to its initial value.

Failure processing may also include obtaining localization informationother than from the localization service. According to some embodiments,the user device can be configured to cache canonical maps. Cached mapspermit devices to access and display content where the cloud isunavailable. For example, cached canonical maps permit device basedlocalization in the event of communication failure or otherunavailability.

According to various embodiments, FIG. 62 describes a high-level flowfor a device initiating cloud-based localization. In other embodiments,various ones or more of the illustrated steps can be combined, omitted,or invoke other processes to accomplish localization and ultimatelyvisualization of virtual content in a view of a respective device.

Further, it should be appreciated that, though the process 6200 showsthe device determining whether to initiate localization at decisionblock 6207, the trigger for initiating localization may come fromoutside the device, including from the localization service. Thelocalization service, for example, may maintain information about eachof the devices that is in a session with it. That information, forexample, may include an identifier of a canonical map to which eachdevice most recently localized. The localization service, or othercomponents of the XR system, may update canonical maps, including usingtechniques as described above in connection with FIG. 26. When acanonical map is updated, the localization service may send anotification to each device that most recently localized to that map.That notification may serve as a trigger for the device to requestlocalization and/or may include updated transformations, recomputedusing the most recently sent sets of features from the device.

FIGS. 63A, B, and C are an example process flow showing operations andcommunication between a device and cloud services. Shown at blocks 6350,6352 6354, and 6456 are example architecture and separation betweencomponents participating in the cloud-based localization process. Forexample, the modules, components, and/or software that are configured tohandle perception on the user device are shown at 6350 (e.g., 660, FIG.6A). Device functionality for persisted world operations is shown at6352 (including, for example, as described above and with respect topersisted world module (e.g., 662, FIG. 6A)). In other embodiments, theseparation between 6350 and 6352 is not needed and the communicationshown can be between processes executing on the device.

Similarly, shown at block 6354 is a cloud process configured to handlefunctionality associated with passable world/passable world modeling(e.g., 802, 812, FIG. 26). Shown at block 6356 is a cloud processconfigured to handle functionality associated with localizing a device,based on information sent from a device, to one or more maps of arepository of stored canonical maps.

In the illustrated embodiment, process 6300 begins at 6302 when a newsession starts. At 6304 sensor calibration data is obtained. Thecalibration data obtained can be dependent on the device represented at6350 (e.g., number of cameras, sensors, positioning devices, etc.). Oncethe sensor calibration is obtained for the device, the calibrations canbe cached at 6306. If device operation resulted in a change in frequencyparameters (e.g., collection frequency, sampling frequency, matchingfrequency, among other options) the frequency parameters are reset tobaseline at 6308.

Once the new session functions are complete (e.g., calibration, steps6302-6306) process 6300 can continue with capture of a new frame 6312.Features and their corresponding descriptors are extracted from theframe at 6314. In some examples, descriptors can comprise DSF's, asdiscussed above. According to some embodiments, the descriptors can havespatial information attached to them to facilitate subsequent processing(e.g., transform generation). Pose information (e.g., information,specified relative to the device's tracking map for locating thefeatures in the physical world as discussed above) generated on thedevice can be appended to the extracted descriptors at 6316.

At 6318, the descriptor and pose information is added to a buffer. Newframe capture and addition to the buffer shown in steps 6312-6318 isexecuted in a loop until a buffer size threshold is exceeded at 6319.Responsive to a determination that the buffer size has been met, alocalization request is communicated from the device to the cloud at6320. According to some embodiments, the request can be handled by apassable world service instantiated in the cloud (e.g., 6354). Infurther embodiments, functional operations for identifying candidatecanonical maps can be segregated from operations for actual matching(e.g., shown as blocks 6354 and 6356). In one embodiment, a cloudservice for map filtering and/or map ranking can be executed at 6354 andprocess the received localization request from 6320. According to oneembodiment, the map ranking operations are configured to determine a setof candidate maps at 6322 that are likely to include a device'slocation.

In one example, the map ranking function includes operations to identifycandidate canonical maps based on geographic attributes or otherlocation data (e.g., observed or inferred location information). Forexample, other location data can include Wi-Fi signatures or GPSinformation.

According to other embodiments, location data can be captured during across reality session with the device and user. Process 6300 can includeadditional operations to populate a location for a given device and/orsession (not shown). For example, the location data may be stored asdevice area attribute values and the attribute values used to selectcandidate canonical maps proximate to the device's location.

Any one or more of the location options can be used to filter sets ofcanonical maps to those likely to represent an area including thelocation of a user device. In some embodiments, the canonical maps maycover relatively large regions of the physical world. The canonical mapsmay be segmented into areas such that selection of a map may entailselection of a map area. A map area, for example may be on the order oftens of meters squared. Thus, the filtered set of canonical maps may bea set of areas of the maps.

According to some embodiments, a localization snapshot can be built fromthe candidate canonical maps, posed features, and sensor calibrationdata. For example, an array of candidate canonical maps, posed features,and sensor calibration information can be sent with a request todetermine specific matching canonical maps. Matching to a canonical mapcan be executed based on descriptors received from a device and storedPCF data associated with the canonical maps.

In some embodiments, a set of features from the device is compared tosets of features stored as part of the canonical map. The comparison maybe based on the feature descriptors and/or pose. For example, acandidate set of features of a canonical map may be selected based onthe number of features in the candidate set that have descriptorssimilar enough to the descriptors of the feature set from the devicethat they could be the same feature. The candidate set, for example, maybe features derived from an image frame used in forming the canonicalmap.

In some embodiments, if the number of similar features exceeds athreshold, further processing may be performed on the candidate set offeatures. Further processing may determine the degree to which the setof posed features from the device can be aligned with the features ofthe candidate set. The set of features from the canonical map, like thefeatures from the device, may be posed.

In some embodiments, features are formatted as a highly dimensionalembedding (e.g., DSF, etc.) and may be compared using a nearest neighborsearch. In one example, the system is configured (e.g., by executingprocess 6200 and/or 6300) to find the top two nearest neighbors usingEuclidian distance, and may execute a ratio test. If the closestneighbor is much closer than the second closest neighbor, the systemconsiders the closest neighbor to be a match. “Much closer” in thiscontext may be determined, for example, by the ratio of Euclideandistance relative to the second nearest neighbor is more than athreshold times the Euclidean distance relative to the nearest neighbor.Once a feature from the device is considered to be a “match” to afeature in canonical map, the system may be configured to use the poseof the matching features to compute a relative transformation. Thetransformation developed from the pose information may be used toindicate the transformation required to localize the device to thecanonical map.

The number of inliers may serve as an indication of the quality of thematch. For example, in the case of DSF matching, the number of inliersreflects the number of features that were matched between receiveddescriptor information and stored/canonical maps. In furtherembodiments, inliers may be determined in this embodiment by countingthe number of features in each set that “match.”

An indication of the quality of a match may alternatively oradditionally be determined in other ways. In some embodiments, forexample, when a transformation is computed to localize a map from adevice, which may contain multiple features, to a canonical map, basedon relative pose of matching features, statistics of the transformationcomputed for each of multiple matching features may serve as qualityindication. A large variance, for example, may indicate a poor qualityof match. Alternatively or additionally, the system may compute, for adetermined transformation, a mean error between features with matchingdescriptors. The mean error may be computed for the transformation,reflecting the degree of positional mismatch. A mean squared error is aspecific example of an error metric. Regardless of the specific errormetric, if the error is below a threshold, the transformation may bedetermined to be usable for the features received from the device, andthe computed transformation is used for localizing the device.Alternatively or additionally, the number of inliers may also be used indetermining whether there is a map that matches a device's positionalinformation and/or descriptors received from a device.

As noted above, in some embodiments, a device may send multiple sets offeatures for localization. Localization may be deemed successful when atleast a threshold number of sets of features match, with an error belowa threshold, and/or a number of inliers above a threshold, a set offeatures from the canonical map. That threshold number, for example, maybe three sets of features. However, it should be appreciated that thethreshold used for determining whether a sufficient number of sets offeatures have suitable values may be determined empirically or in othersuitable ways. Likewise, other thresholds or parameters of the matchingprocess, such as degree of similarity between feature descriptors to bedeemed matching, the number of inliers for selection of a candidate setof features, and/or the magnitude of the mismatch error, may similarlybe determined empirically or in other suitable ways.

Once a match is determined, a set of persistent map features associatedwith the matched canonical map or maps is identified. In embodiments inwhich the matching is based on areas of maps, the persistent mapfeatures may be the map features in the matching areas. The persistentmap features may be persistent poses or PCFs as described above. In theexample of FIG. 63, the persistent map features are persistent poses.

Regardless of the format of the persistent map features, each persistentmap feature may have a predetermined orientation relative to thecanonical map in which it is a part. This relative orientation may beapplied to the transformation computed to align the set of features fromthe device with the set of features from the canonical map to determinea transformation between the set of features from the device and thepersistent map feature. Any adjustments, such as might be derived fromcalibration data, may then be applied to this computed transformation.The resulting transformation may be the transformation between the localcoordinate frame of the device and the persistent map feature. Thiscomputation may be performed for each persistent map feature of amatching map area, and the results may be stored in a table, denoted asthe persistent_pose_table in 6326.

In one example, block 6326 returns a table of persistent posetransforms, canonical map identifiers, and number of inliers. Accordingto some embodiments, the canonical map ID is an identifier for uniquelyidentifying a canonical map and a version of the canonical map (or areaof a map, in embodiments in which localization is based on map areas).

In various embodiments, the computed localization data can be used topopulate localization statistics and telemetry maintained by thelocalization service at 6328. This information may be stored for eachdevice, and may be updated for each localization attempt, and may becleared when the device's session ends. For example, which maps werematched by a device can be used to refine map ranking operations. Forexample, maps covering the same area to which the device previouslymatched may be prioritized in the ranking. Likewise, maps coveringadjacent areas may be give higher priority over more remote areas.Further, the adjacent maps might be prioritized based on a detectedtrajectory of the device over time, with map areas in the direction ofmotion being given higher priority over other map areas. Thelocalization service may use this information, for example, upon asubsequent localization request from the device to limit the maps or mapareas searched for candidate sets of features in the stored canonicalmaps. If a match, with low error metrics and/or a large number orpercentage of inliers, is identified in this limited area, processing ofmaps outside the area may be avoided.

Process 6300 can continue with communication of information from thecloud (e.g., 6354) to the user device (e.g., 6352). According to oneembodiment, a persistent pose table and canonical map identifiers arecommunicated to the user device at 6330. In one example, the persistentpose table can be constructed of elements including at least a stringidentifying a persistent pose ID and a transform linking the device'stracking map and the persistent pose. In embodiments in which thepersistent map features are PCFs the table may, instead, indicatetransformations to the PCFs of the matching maps.

If localization fails at 6336, process 6300 continues by adjustingparameters that may increase the amount of data sent from a device tothe localization service to increases the chances that localization willsucceed. Failure, for example, may be indicated when no sets of featuresin the canonical map can be found with more than a threshold number ofsimilar descriptors or when the error metric associated with alltransformed sets of candidate features is above a threshold. As anexample of a parameter that may be adjusted, the size constraint for thedescriptor buffer may be increased (of 6319). For example, where thedescriptor buffer size is five, localization failure can trigger anincrease to at least six sets of features, extracted from at least siximage frames. In some embodiments, process 6300 can include a descriptorbuffer increment value. In one example, the increment value can be usedto control the rate of increase in the buffer size, for example,responsive to localization failures. Other parameters, such asparameters controlling the rate of localization requests, may be changedupon a failure to find matching canonical maps.

In some embodiments, execution of 6300 can generate an error conditionat 6340, which includes execution where the localization request failsto work, rather than return a no match result. An error, for example,may occur as a result of a network error making the storage holding adatabase of canonical maps unavailable to a server executing thelocalization service or a received request for localization servicescontaining incorrectly formatted information. In the event of an errorcondition, in this example, the process 6300 schedules a retry of therequest at 6342.

When a localization request is successful, any parameters adjusted inresponse to a failure may be reset. At 6332, process 6300 can continuewith an operation to reset frequency parameters to any default orbaseline. In some embodiments 6332 is executed regardless of any changesthus ensuring baseline frequency is always established.

The received information can be used by the device at 6334 to update acache localization snapshot. According to various embodiments, therespective transforms, canonical maps identifiers, and otherlocalization data can be stored by the device and used to relatelocations specified with respect to the canonical maps, or persistentmap features of them such as persistent poses or PCFs to locationsdetermined by the device with respect to its local coordinate frame,such as might be determined from its tracking map.

Various embodiments of processes for localization in the cloud canimplement any one or more of the preceding steps and be based on thepreceding architecture. Other embodiments may combine various ones ormore of the preceding steps, execute steps simultaneously, in parallel,or in another order.

According to some embodiments, localization services in the cloud in thecontext of cross reality experiences can include additionalfunctionality. For example, canonical map caching may be executed toresolve issues with connectivity. In some embodiments, the device mayperiodically download and cache canonical maps to which it haslocalized. If the localization services in the cloud are unavailable,the device may run localizations itself (e.g., as discussedabove—including with respect to FIG. 26). In other embodiments, thetransformations returned from localization requests can be chainedtogether and applied in subsequent sessions. For example, a device maycache a train of transformations and use the sequence of transformationsto establish localization.

Various embodiments of the system can use the results of localizationoperations to update transformation information. For example, thelocalization service and/or a device can be configured to maintain stateinformation on a tracking map to canonical map transformations. Thereceived transformations can be averaged over time. According to oneembodiment, the averaging operations can be limited to occur after athreshold number of localizations are successful (e.g., three, four,five, or more times). In further embodiments, other state informationcan be tracked in the cloud, for example, by a passable world module. Inone example, state information can include a device identifier, trackingmap ID, canonical map reference (e.g., version and ID), and thecanonical map to tracking map transform. In some examples, the stateinformation can be used by the system to continuously update and getmore accurate canonical map to tracking map transforms with everyexecution of the cloud-based localization functions.

Additional enhancements to cloud-based localization can includecommunicating to devices outliers in the sets of features that did notmatch features in the canonical maps. The device may use thisinformation, for example, to improve its tracking map, such as byremoving the outliers from the sets of features used to build itstracking map. Alternatively or additionally, the information from thelocalization service may enable the device to limit bundle adjustmentsfor its tracking map to computing adjustments based on inlier featuresor to otherwise impose constraints on the bundle adjustment process.

According to another embodiment, various sub-processes or additionaloperations can be used in conjunction and/or as alternatives to theprocesses and/or steps discussed for cloud-based localization. Forexample, candidate map identification may include accessing canonicalmaps based on area identifiers and/or area attributes stored withrespective maps.

Dense Map Merge

Described herein are methods and apparatus for efficiently generatingand sharing 3D reconstructions that enable use of an XR system in largescale environments and with immersive multi-user experiences, even withuser devices of limited computational resources. 3D reconstructions mayinclude 3D representations of physical environments, which may be usedfor XR functions such as visual occlusion, physics-based interactionswith virtual objects, and/or environmental reasoning.

The inventors have recognized and appreciated methods and apparatus thatbuild, store, and share large scale 3D reconstruction. These techniquesmay operate in conjunction with systems that enable multiple devices toshare pose information through a collection of maps. The maps used toprovide pose information, such as shared canonical maps described above,may be sparse maps. Sparse maps may represent the physical world basedon only a subset of information about the world, such as the sets offeatures used in the shared canonical maps and tracking maps describedabove.

The sparse maps may provide a common frame of reference for both devicescollecting dense information about the 3D environment and sharedprocessing resources, processing that dense information from multipledevices into a large-scale dense representation of the 3D environment.Those shared processing resources may be implemented as a cloud service,as described above in connection with sparse map construction andlocalization.

For example, each device that localizes to a canonical map may obtain aset of persistent poses, providing location and orientation information(which, in some embodiments, may be formatted as PCFs as describedabove). The persistent poses may be related to the local coordinateframe of the device through a transformation generated through alocalization process, as described above. Dense information about the 3Denvironment collected on each of multiple devices may be posed such thatit has information associated with a location and orientation withrespect to a frame of reference. As a result of transformationsgenerated through the localization process, the poses of the denseinformation may be related to the shared persistent poses. Thistransformation may be applied on the device or in the cloud. When theposed dense information is processed in the cloud, a cloud processingcomponent can group dense information about the same region of the 3Denvironment for processing together. Such a cloud processing component,referred to as a dense map merge or stitch component herein, may processthe groups of dense information separately to construct multipleportions of a large-scale reconstruction of the 3D environment. In someembodiments, dense information collected on multiple devices referencedto the same persistent location in a shared sparse map may be processedtogether to form a portion of the large-scale dense reconstruction.

That large scale reconstruction may serve as a shared dense map.Portable devices may access the shared dense map, such that the devicescan obtain dense information about the 3D environment with low delay andless processing than would otherwise be required to reconstruct thatdense information from sensor data.

Pose information derived through a sparse map may also aid in managinginteractions between devices and a cloud service such that each deviceobtains dense map information applicable to its location. In embodimentsin which there are multiple sparse maps managed by a cloud service, forexample, there may be multiple dense maps, each with dense informationreferenced to the persistent poses of one of the sparse maps.Localization of a device with respect to a sparse map, as describedabove, may result in identification of a dense map, corresponding tothat sparse map, containing dense information applicable to the devicein its current location.

In some embodiments, devices in the system may include a cachingstructure for efficient access to shared 3D reconstructions built on thecloud. Each device, for example, may retrieve and store an applicabledense map associated with its location, as determined through thelocalization process. The device may then access dense information inthe shared map from local storage faster than over a network.

In some embodiments, devices may cache only portions of a shared densemap. The cached portions also may be identified based on informationgenerated during sparse map localization. In embodiments in which densemaps contain surface information posed with respect to persistent posesin a sparse map, localization results expressed relative to a persistentpose in the shared sparse map enable selection of surface informationrelevant to each device. Each device, as it localizes with respect to apersistent pose from the shared sparse map, may update its cache so asto include the dense map portions referenced to the same persistentpose. In some embodiments, the device may maintain a cache to includedense map portions referenced to the same persistent pose against whichlocalization was achieved, and optionally, neighboring persistent poses.

Devices may combine cached dense map information with local 3Dreconstruction built on the device to provide efficient and accurateaccess to dense map information. As the devices operate, they maylocally perform 3D reconstructions. These locally generated 3Dreconstructions may be sent to the cloud for merging or stitching intothe shared dense maps. In some scenarios, a device may select to use itslocally generated 3D reconstruction rather than a shared dense map. Forexample, a device may use its locally generated 3D representation if ithas a local dense representation of a portion of the 3D environment forwhich it has no cached portion of the shared dense map, the device mayuse its local dense representation. In scenarios in which the device hasupdated its local dense representation for which it has cached a portionof the shared dense map after that portion of the shared dense map wasupdated in the cloud, the device may use its local dense representation.

In some embodiments, devices may update a 3D environment representationdownloaded from the cloud with a subset of historical depth and poseinformation accumulated while the 3D representation is being built onthe cloud, as well as new pose and depth information. Consequently, eachdevice can have a shared but also tailor-made 3D reconstructionreflecting up-to-date real-world geometries. In some embodiments,devices may include a filesystem that stores the downloaded sharedreconstruction and the local reconstruction in a way that avoidsduplicated environment mapping when entering a previously exploredspace. Once the environment representation from the cloud is updated toreflect historical depth and pose information accumulated by a device,that accumulated information may be deleted from the device.Alternatively or additionally, once the device detects changes in aregion of the environment represented by a portion of the downloadedrepresentation, that portion of the downloaded representation may beupdated.

In some embodiments, devices may have low computational overhead basedon the way the 3D reconstruction is built on the cloud. The 3Drepresentations may be divided into smaller volumes such that individualdevices can first download from the cloud volumes visible to thedevices, which provides real time performance on device, minimizes thenetwork bandwidth needed when fetching shared reconstruction, and makesit scalable for very large environments. Alternatively or additionally,in some embodiments, surface information in a 3D reconstruction may beformatted to facilitate separate processing of information on surfacesthat are likely to be persistent. As a result, low computer resources,such as computation and network bandwidth, may be used for persistentsurfaces.

These techniques may be applied to shared reconstructions and/or localreconstructions. For example, identifiable objects, such as planarsurface geometries, may be assigned persistent unique identifiers acrossmultiple sessions such that virtual content can be associated with andpersisted by the objects with persistent unique identifiers.

In some embodiments, each device may build its own local reconstructionusing pose tracking and depth image information captured by the devicein real time. The depth information, or other representation of surfacesin the vicinity of the device, may be posed. Initially, that informationmay be posed with respect to a local coordinate frame of the trackingmap of the device. However, these local reconstructions may be posedrelative to the persistent poses shared by devices in the system througha localization process, such as persistent coordinate frames of thecanonical maps as described above. It should be appreciated thatprocessing to transform depth information posed relative to a devicecoordinate frame to depth information posed relative to a sharedcoordinate frame may be performed on the device or on the cloud. Asdescribed above, information sufficient to compute a suitabletransformation (such as a set of features posed with respect to thelocal coordinate frame or a tracking map of the device) is sent from thedeice to the cloud in connection with using, building, or maintainingcanonical sparse maps. In some embodiments, the transformation may beapplied to depth information on the device. In some embodiments, thetransformation may alternatively or additionally be applied in thecloud. Depth information may similarly be sent to the cloud inconnection with such information such that the transformation is appliedby computing resources in the cloud. Alternatively or additionally, itis also described above that a transformation may be sent to each deviceand applied on the device.

When the device is connected to a network, each depth image, which is orcan be associated to a coordinate frame of a shared sparse map, may beuploaded to the cloud. Cloud processing can then identify depth imagesrepresenting the same portions of the physical world. A high-quality 3Dreconstruction can be built on the cloud, using those uploaded depthimages.

As a specific example, the dense map may be built in connection with asparse map merge or stitch function as described above. Depth imagescaptured on each device may be posed relative to persistent coordinateframes in the tracking maps of the respective devices. When the trackingmaps are sent to the cloud for merging or stitching into a merged orstitched sparse map, the depth images may similarly be sent. Sparse mapmerge or stitch processing may identify that devices have tracking mapswith persistent coordinate frames representing overlapping portions ofthe 3D environment. When multiple devices have overlapping persistentcoordinate frames, the persistent coordinate frames may be combined andstill stay persistent afterwards. A portion of the 3D reconstruction maybe built from the depth information, from multiple devices, that isassociated to any of the persistent coordinate frames that are combined.

A 3D representation in the 3D reconstruction can be divided into smallervolumes, and these smaller volumes may be fetched by devices. The sharedand/or local reconstruction can be periodically stored to the device'sfilesystem so that when a device enters an identified space, thecorresponding data can be loaded from the device's filesystem to thedevice's memory that may be accessed by XR applications.

In some embodiments, when a device enters an explored space and itsfilesystem has a local reconstruction of the space, the device can fetch3D representations of the space from the filesystem. Additionally oralternatively, when the space has been reconstructed on the cloud, thedevice can directly fetch 3D representations of the space from theshared reconstruction on the cloud, or from its filesystem if the 3Drepresentations of the space have been previously fetched and stored,without building 3D reconstruction of the space again from scratch. Insome embodiments, devices may adjust downloaded 3D representations ofthe space using a subset of the pose and depth data accumulated afterthe 3D representations of the space have been built on the cloud.

FIG. 64 is a block diagram of an XR system 6400 that provides largescale 3D environment reconstruction, according to some embodiments. TheXR system 6400 may include a cloud 6402 and one or more devices 6404that may communicate with the cloud 6402 through a network. In thisexample, a single device is illustrated for simplicity, but multipledevices with like components (e.g., the components shown in FIG. 64) mayinteract with the cloud.

The device 6404 may include an on-device 3D reconstruction component6460 (e.g., 516 in FIG. 3) configured to generate local 3Dreconstruction of environments visited by the device, and an on-devicedense map handling component 6900 configured to merge or stitch adownloaded shared 3D reconstruction with a local (e.g., on device) 3Dreconstruction. The device 6404 may include a caching structure 6454that may store environment information including, for example, posedsensor data 6422, sparse tracking maps 6412, and local dense maps 6432(See FIG. 73). In some embodiments, the posed sensor data 6422 may beacquired from a depth sensor, but in other embodiments may be acquiredfrom vision sensors or other sensors outputting data from which surfacesmight be identified. Metadata may be stored in association with thisenvironment information or may form a portion of the environmentinformation. For example, sparse tracking map 6412 may include apersistent pose table 6413, listing persistent poses in the tracking mapand unique identifiers for each persistent pose and/or other informationas described above. As another example, posed sensor data 6422 mayinclude sensor data that has associated with the data defining a poserelative to a persistent pose of the device's tracking map and a uniqueid of that persistent pose.

The cloud 6402 may include a cloud sparse map merge or stitch component6414 configured to merge or stitch sparse maps, as described above. Thesparse maps that are merged or stitched may include sparse tracking mapsreceived from the devices 6404, which may be merged or stitched witheach other and/or with shared sparse maps on the cloud. Cloud 6402 mayalso include a cloud dense map merge or stitch component 6440 configuredto merge or stitch dense maps. Portions of local dense maps sent fromdevices 6404 may be merged or stitched with each other and/or withshared dense maps already stored on the cloud. In some embodiments, theinformation may include or be limited to depth images and detectedplanes, along with metadata, such as poses for the images and planes.Further, the information sent may be a subset of the information of thistype maintained on the device. For example, only information associatedwith one or more persistent poses in the device's tracking map may besent at one time.

The cloud dense map merge or stitch component 6440 may share components6450 with the cloud sparse map merge or stitch component 6414. The cloud6402 may include a passable world component 6452 that may store theshared sparse maps 6416 and the shared dense maps 6442.

In some embodiments, each device 6404 may build a sparse tracking map6412 as the device moves around a physical environment. The device maymaintain one or more sparse tracking maps. Each sparse tracking map 6412may include persistent poses, identifiable by descriptors (e.g., PP ID),and a persistent pose table that indicates correspondence between thepersistent poses and their descriptors. Each posed sensor data 6422 mayinclude image data, a descriptor of a persistent pose of a sparsetracking map that is associated with the depth image, and atransformation between the persistent pose and a headpose of the devicewhen the depth image is captured.

Local sparse tracking maps 6412 may be sent to the cloud 6402 through anetwork by a device 6404 for functions such as localizing the device inlarge scale environments, and building shared sparse maps for largescale environments. The cloud sparse map merge or stitch component 6414may generate shared sparse maps 6416 based on one or more local sparsetracking maps 6412 sent to the cloud by one or more devices 6404. Insome embodiments, the cloud sparse map merge or stitch component 6414may include components configured to perform portions of method 3700 ofFIG. 37 and/or process 6300 of FIG. 63.

In some embodiments, each shared sparse map 6416 may include metadata6418 and persistent pose table 6420. The metadata 6418 may include a mapidentifier (e.g., map ID) for the shared sparse map such that the sharedsparse map can be accessed by any device in the system based on the mapidentifier. In some embodiments, the map identifier may include a uniquedescriptor (e.g., a 128-bit identifier) that indicates a correspondingunique area of a 3D environment, and a version descriptor that indicateswhen the map was built and/or last updated. The persistent pose table6420 may include merged or stitched persistent poses, created based onpersistent poses from sparse tracking maps and identifiable bypersistent pose descriptors. The persistent pose table 6420 may alsoinclude a persistent pose table that indicates correspondences betweenthe merged or stitched persistent poses and their descriptors.

In some embodiments, each device 6404 may build a local dense map 6432indicating surface information about portions of a physical environment.The device may maintain one or more local dense maps. The on-device 3Dreconstruction component 6460 may generate local dense maps 6432 basedat least in part on posed sensor data 6422 (e.g., depth maps 512 of FIG.3) and sparse tracking maps 6412 (e.g., tracking map 700 of FIG. 7). Forexample, depth information associated with the same persistentcoordinate frame of the tracking map may be processed together to form a3D reconstruction of a region of the 3D environment in the vicinity ofthe location represented by that persistent coordinate frame. The localdense maps 6432 may include surface information, which may berepresented in one or more formats. In the illustrated example thesurface information is represented as volumetric data 6436 (e.g.,volumetric information 662 a), meshes 6456 (e.g., meshes 662 c), andobjects information 6438 (e.g., planes 662 d). The dense maps may alsoinclude metadata 6434 (e.g., volumetric metadata 662 b).

Portions of local dense maps 6432 may be sent to the cloud 6402 througha network by a device 6404 for functions such as building shared densemaps for large scale environments. The cloud dense map merge or stitchcomponent 6440 may generate shared dense maps 6442 based on informationsent to the cloud by one or more devices 6404. In some embodiments,information for the shared dense maps 6442 may be generated based onposed sensor data 6422 and local sparse maps 6412. Alternatively oradditionally, shared dense maps 6442 may be generated based on portionsof local dense maps 6432. Component 6440 may enable dense maps to begenerated and shared in the XR system. Component 6440 may persist the 3Dreconstruction on the cloud for future accesses such that no redundant3D reconstruction is built. In some scenarios, such as when the shareddense maps 6442 are generated based on posed sensor data 6422 and localsparse maps 6412, component 6440 may relieve the devices fromcomputationally intensive 3D reconstruction,

In some embodiments, each shared dense map 6442 may include metadata6444, volumetric data 6446, meshes 6458, and objects information 6448.The metadata 6444 may include a map identifier (e.g., map ID) for theshared dense map such that the shared dense map can be accessed by anydevice in the system based on the map identifier. In some embodiments,the map identifier may include a unique descriptor (e.g., a 128-bitidentifier) that indicates a corresponding unique area of a physicalenvironment. For a same unique area of a physical environment, acorresponding sparse map and a corresponding dense map may have a sameunique descriptor. The map identifier may also include a versiondescriptor that indicates when the map was built and/or last updated.

Shared dense maps 6442 may be accessed by one or more devices 6404 inthe XR system. A device 6404 may download one or more shared dense maps6442 corresponding to its location. In embodiments in which the densemaps are segmented into smaller volumes, the devices may downloadinformation for one, or a small number of those smaller volumes. Theon-device dense map handling component 6900 may receive the downloadedshared dense maps 6442. The on-device dense map handling component 6900may update portions of the downloaded shared dense maps 6442 based atleast in part on posed sensor data 6422 and sparse tracking maps 6412that may be captured when the portions of the downloaded shared densemaps are being built on the cloud and/or after the portions have beenbuilt. The on-device dense map handling component 6900 may store theupdated dense map as a new local dense map 6432. Component 6900 enablesthe devices 6404 to have 3D reconstruction that corresponds to real-timechanges. (See FIG. 69).

FIG. 65A is a block diagram of at least a portion 6500 of the cloudsparse map merge or stitch component 6414, according to someembodiments. The at least a portion 6500 of the cloud sparse map mergeor stitch component 6414 may include a persistent pose merger orstitching component 6508 configured to combine persistent poses frommultiple sparse maps, for example, sparse tracking maps 6502, 6504, and6506 as illustrated. The persistent pose merger or stitching component6508 may provide a merged or stitched sparse map 6510 based on thesparse maps to be merged or stitched, which in this example are sparsetracking maps 6502, 6504, and 6506. In some embodiments, other types ofsparse maps, such as previously stored maps may also be inputs to mergeor stitch processing. Each sparse tracking map may include a devicecoordinate frame that is local to the device building the tracking map.The merged or stitched sparse map may include a canonical coordinateframe that is shared by the maps stored on the cloud. The merged orstitched sparse map 6510 may include a merged or stitched persistentpose table in a coordinate frame of the merged or stitched sparse map.

The portion 6500 of cloud sparse map merge or stitch component 6414 mayinclude a sparse map localization component 6512 configured to providelocalization results 6514 for one or more sparse tracking maps to amerged or stitched sparse map and/or a shared sparse map already storedon the cloud. The localization results 6514 may include transformationsbetween coordinate frames used by individual sparse maps and thecanonical coordinate frame. In some embodiments, the sparse maplocalization component 6520 may also provide map identifiers for thesparse tracking maps and the merged or stitched sparse map.

It should be appreciated that although the persistent pose merger orstitching component 6508 and the sparse map localization component 6512are illustrated, the cloud sparse map merge or stitch component 6414 mayinclude alternative and/or additional components. For example,transformations that may be used to relate depth information collectedin a device's local coordinate frame to a canonical coordinate frame maybe computed in other ways. For example, as described above, atransformation is provided as a result of a localization process thatmay be performed for the device more frequently than a tracking map issent to the cloud for merging or stitching. The transformationsgenerated as a result of localization against a stored map may be usedto relate dense information collected on the device to a shared densemap that also has a frame of reference that can be related to that ofthe stored map. As another example, it should be appreciated that theprocessing illustrated in FIG. 65A need not be performed concurrently.For example, the persistent poses of the local tracking maps that aremerged or stitched into persistent poses in the merged or stitchedsparse map may be identified over time as individual tracking maps aremerged or stitched into the merged or stitched sparse map.

FIG. 65B is a schematic diagram illustrating a merged or stitched sparsemap 6510 provided by the persistent pose merger or stitching component6508, according to some embodiments. FIG. 65C is another schematicdiagram of the merged or stitched sparse map 6510, illustrating thelocalization results 6514 provided by the sparse map localizationcomponent 6414, according to some embodiments.

In the example illustrated in FIG. 65A, the persistent pose merger orstitching component 6508 may receive the three sparse tracking maps6502, 6504, and 6506 of a physical environment 6530. The three sparsetracking maps 6502, 6504, and 6506 may be captured by one device overthree different sessions, or by three devices over individual devicesessions, or any suitable combination of number of devices and number ofsessions. Each sparse tracking map may include persistent poses 6522,each of which may have one or more keyrigs 6524 associated with it. Eachsparse tracking map may also include feature points 6526 extracted fromimages such as keyrigs 6524. The features may be, as described aboverepresented as descriptors and may be posed.

Each persistent pose includes stored information, such as the features,that a device can compare to current image information to determinewhether the device is in the vicinity of a location represented by thepersistent pose. Multiple persistent poses that represent locations thatare close together may be redundant, if a device may determine itslocation with respect to any of the persistent poses. The persistentpose merger or stitching component 6508 may combine the persistent posesfrom each sparse tracking map by, for example, removing redundantpersistent pose. For example, sparse tracking map 6502 may include apersistent pose 6522C, which may also be included in sparse tracking map6506. The persistent pose merger or stitching component 6508 may removeone of the persistent poses 6522C such that the resulting persistentpose table for the merged or stitched sparse map 6510 may include onlyone merged or stitched persistent pose corresponding to 6522C. The cloudsparse map merge or stitch component 6414 may provide threetransformations: T1 for sparse tracking map 6502 to the merged orstitched sparse map 6510, T2 for sparse tracking map 6504 to the mergedor stitched sparse map 6510, and T3 for sparse tracking map 6506 to themerged or stitched sparse map 6510.

FIG. 66 and FIG. 67 are block diagrams of examples 6600 and 6700 of thecloud dense map merge or stitch component 6440. The cloud dense mapmerge or stitch component 6440 may receive and/or generate collectionsof surface information. The collections of surface information mayinclude posed sensor captured data such as depth images and the depthimages' pose. The pose may be expressed in the form of a pose relativeto a persistent pose of a sparse tracking map and the identifier for thepersistent pose, which in some embodiments may include the identifier ofthe corresponding sparse tracking map. However, as an XR system asdescribed herein may compute and apply transformations between local andshared coordinate frames, depth information, and other surfaceinformation, may be posed with respect to other coordinate frames byapplying appropriate transformations.

The cloud sparse map merge or stitch component 6414 may identify one ormore shared sparse maps that overlap with the sparse tracking map, andmerge or stitch the sparse tracking map with one or more shared sparsemaps. For this newly generated merged or stitched shared sparse map, thecloud dense map merge or stitch component 6440 may build a correspondingshared dense map based on the merged or stitched persistent poses of themerged or stitched shared sparse map and depth images associated withthose merged or stitched persistent poses. The cloud dense map merge orstitch component 6440 may convert the depth images' poses to be relativeto a merged or stitched persistent pose based on a computedtransformation between a device coordinate frame local to the device andthe canonical coordinate frame such that each depth image may have acamera frustum in the canonical coordinate frame. Consequently, thecloud dense map merge or stitch component 6440 may use the depth imagesand their poses for building 3D reconstructions in the canonicalcoordinate frame.

A merged or stitched dense map may be constructed from surfaceinformation collected by multiple devices. That surface information maybe formatted in any of multiple ways. Surface information, may beformatted as posed depth images. The depth images may provide volumetricdata, which may be formatted to indicate for each of multiple voxelsdefining a volume whether a surface was detected in a locationrepresented by that voxel. Alternatively or additionally, surfaceinformation may be represented as objects, such as planes or meshes. Insome embodiments, a dense map merge or stitch component may processsurface information received in different formats.

In the example shown in FIG. 66, surface information is provided asposed sensor data 6604, which are posed with respect to sparse trackingmap 6602. All or a part of a sparse tracking map 6602 may also beprovided as part of the specification of surface information. In theillustrated scenario, a cloud dense map merge or stitch component 6600may include the sparse map localization component 6512 and a 3Dreconstruction component 6606. The sparse map localization 6512 mayprovide localization results 6614 for a received sparse tracking map6602 to one or more shared sparse maps 6416. The localization results6614 and depth images 6606 posed relative to the sparse tracking map6602 enable the pose of the depth images to be related to that of ashared sparse map 6416. A transformation to pose the depth imagesrelated to a coordinate frame of the shared sparse maps may be appliedby 3D reconstruction component 6606 or by another component (not shown)prior to passing the depth images to 3D reconstruction component 6606.Regardless of where the transformation is applied, 3D reconstructioncomponent 6606 may generate one or more shared dense maps 6642 in acoordinate frame of the shared sparse maps.

In the example shown in FIG. 67, surface information is provided in theform of posed sensor data 6702 with associated tracking maps, which maybe represented as a persistent poste table for the tracking map. In someembodiments, surface information may alternatively or additionally beprovided in other forms, such as depth images and/or as denseinformation that has already been reconstructed locally on the devices.In this example, locally generated dense information is indicated as acurrent dense map 6708, and may have the same format as a shared densemap 6742.

The specific processing performed to merge or stitch surface informationfrom one or more devices may depend on the specific format of thesurface information. In this example, a cloud dense map merge or stitchcomponent 6700 may include a sparse tracking map persistent poseselection component 6704, a sparse map selection component 6710, thesparse map localization component 6512, and the 3D reconstructioncomponent 6606.

The sparse tracking map persistent pose selection component 6704 mayselect a subset 6706 of the received posed depth sensor data 6702. Theselection may be based on received sparse tracking map 6502 and a mergedor stitched persistent pose table 6510T of the merged or stitched sparsemap 6510. In some embodiments, the subset of posed sensor data 6702 isselected such that the posed sensor data of the subset is posed relativeto a persistent pose (PP) in the merged or stitched sparse map PP table6510T. These persistent poses may be, for example, persistent poses ofthe sparse tracking map 6502 that have been promoted to merged orstitched persisted poses of the merged or stitched sparse map 6510.Posed depth sensor data 6702 associated with PPs in the merged orstitched sparse map PP table 6510T may be selected for inclusion insubset 6706. Posed sensor data not supplying data within the subset 6706may be deleted because the poses of those posed sensor data may not beconverted to be in the canonical coordinate frame.

In embodiments in which multiple merged or stitched sparse maps aremaintained, sparse map selection component 6710 may select a sparsetracking map based on the information supplied by the device forlocalization. The sparse map localization component 6512 may providelocalization results 6714 for the selected sparse tracking map to themerged or stitched sparse map. Merged sparse map 6510 also may beprovided as an input to 3D reconstruction component 6606, facilitating3D reconstruction based on surface information that is posed relative tothe merged or stitched sparse map or any other frame of reference forwhich a transform to or from the coordinate frame of the merged orstitched sparse map is available. The 3D reconstruction 6606 may computeone or more dense maps 6742 based on the subset 6706 of posed depthsensor data, the localization results 6714, and/or other inputs.

3D reconstruction may alternatively or additionally be based, forexample, on other data about the 3D environment around one or moredevices supplying input for dense map merge or stitch. For example, acurrent dense map 6708 from a device may be supplied to 3Dreconstruction component 6606. In some embodiments, a device may supplyall or parts of its current dense map 6708 that may include objectinformation. In embodiments in which the dense map 6708 has a frame ofreference relative to the local coordinate frame on the device, thedevice may also provide information such that its local coordinate framemay be related to the coordinate frame used for merged or stitchedsparse maps 6510 shared by devices interacting with the XR system.

FIG. 68 is a simplified schematic diagram illustrating a merged orstitched dense map 6800 provided by the cloud dense map merge or stitchcomponent 6440, according to some embodiments. The merged or stitcheddense map 6800 is illustrated in a voxel grid comprising voxels 6802.The voxel grid may include filled voxels that contain signed distancevalues to surfaces nearby, and empty voxels that contain no valuebecause no surface is in their vicinity. In the illustration, the voxelgrid is shown in two dimensions for simplicity of illustration. Thevoxel grid may extend in a third dimension.

In the simplified example, the merged or stitched dense map 6800 may bebased on one shared dense map and three uploaded dense maps. The threeuploaded dense maps may be built over three different sessions by one ormore devices. Before receiving the three uploaded dense maps, the shareddense map may include a filled voxel region 6806A that represents asurface 6804A in a physical environment. The filled voxel region 6806Amay be reconstructed based on previously received surface informationand stored on the cloud after reconstruction.

The cloud may merge or stitch this previously reconstructed shared mapwith the three newly received dense maps. The cloud may receive a firstdense map that includes persistent pose pp1, a second dense map thatincludes persistent poses pp2 and pp4, and a third dense map thatincludes persistent pose pp3. Information that is posed relative to apersistent pose is indicated as encompassed within a correspondingcamera frustum 6808. The information posed relative to a persistent posemay extend for a distance relative to the location represented by thepersistent pose, here indicated as a visibility range 6810. The 3Dreconstruction component 6606 may reconstruct a voxel region 6806B thatrepresents a surface 6804B in the physical environment, and a voxelregion 6806C that represents a surface 6804C in the physicalenvironment.

It should be appreciated that the merged or stitched dense map 6800 isbuilt based on surface information captured at different times by one ormore devices. In the illustrated example, the voxel region 6806B isbuilt based on depth images associated with pp1 and depth imagesassociated with pp2. In this example, pp1 and pp2 may be identified asbeing sufficiently close together that the depth information associatedwith those persistent poses may represent the same objects, and so maybe fused together. For example, pp1 and pp2 may be persistent poses thatare merged or stitched into a persistent pose in a merged or stitchedsparse tracking map. The depth information posed relative to each of pp1and pp2 may be transformed into the coordinate frame of pp5 such thatthe information received as posed relative to pp1 and pp2 is all posedrelative to pp5. The 3D reconstruction component 6606 may fuse posedsensor data, individual depth images or depth information in otherformats such that it may be used to generate surface information. Insome embodiments, the merged or stitched depth information may then beused to compute volumetric data. In this way, a larger volume in thephysical world that may be represented in the merged or stitched densemap than could be represented based on depth information from any of thedevices alone. For example, planes or other objects might be identifiedbased on the computed volumetric data.

In some embodiments, objects generated locally on devices may be matchedto objects generated with the merged or stitched volumetric data. Inthis way, the same object may be given a same object identifier,regardless of where the processing is performed to identify that object.

In other embodiments, surface information other than depth images mayalternatively or additionally be merged or stitched into a shared densemap. Surfaces may be represented in other ways, such as planes or otherobjects and/or as meshes. Different merging or stitching techniques maybe employed for different types of surface information. Merging ofplanes may be performed as described below to merge or stitch planesthat processing indicates likely represent the same surface, such asbecause of similarity of location and plane normal or to add to themerged or stitched dense map all of the planes from the individual mapsbeing merged or stitched that are not indicated as part of the samesurface. Similarly, for meshes—where processing indicates that portionsof two or more meshes likely represent the same portion of a surface,those meshes may be merged or stitched into a combined mesh. Conversely,meshes with no surfaces overlapping with other meshes may be addedunchanged to the merged or stitched map.

In some embodiments, merging or stitching of surface information mayentail removing duplicate surface information, regardless of the formatin which it is represented. For example, if a dense map from a deviceindicates a plane in a same location as volumetric data from anotherdevice indicates a surface, that surface may be removed from thevolumetric data before processing or otherwise not added to the mergedor stitched dense map. Likewise, if a mesh in one dense map isdetermined to likely correspond to the same surfaces as a plane orvolumetric data, that plane or volumetric data may be removed orotherwise not added to the merged or stitched dense map. In someembodiments in which surface information may be represented in differentformats, the surface information may be processed during the merge orstitch operation in a hierarchical fashion, with surface information atlower levels of the hierarchy not being included in a merged or stitchedmap where the same surfaces are represented at higher levels of thehierarchy. For example, meshes may be processed first, then planes andthen volumetric data.

As can be seen in FIG. 68, the voxels have a determinable location withrespect to persistent locations, which may be related to locations in amerged or stitched shared sparse map. Accordingly, portions of themerged or stitched dense map representing portions of the physical worldnear a device that has localized to a sparse shared map may beidentified. The shared dense maps on the cloud may be accessed bydevices according to their localization results provided by the sparsemap localization component. A device, for example, may downloadvolumetric data computed from merged or stitched depth information anduse that merged or stitched volumetric data for computing arepresentation of surface in the 3D environment. A device may merge orstitch that merged or stitched volumetric data with volumetric datacaptured with its sensors. This may be performed in dense map handlingcomponent 6900 of FIG. 64, for example.

In embodiments, in which dense maps include planes or other objects, adevice may merge or stitch the objects of the downloaded shared densemaps with objects in local dense maps instead of or in addition tomerging or stitching volumetric data. The on-device dense map handlingcomponent 6900 shown in FIG. 69 may receive metadata of a merged orstitched dense map, and sparse map localization results that indicate atleast one transformation from the sparse tracking map to a merged orstitched sparse map. When a sparse map management component on thedevice determines that the device can move to the merged or stitchedsparse map, the corresponding merged or stitched dense map may beindicated as a candidate for dense map localization. Based at least inpart on the received metadata, a dense map management component on thedevice may determine whether the device should move to the merged orstitched dense map. When the dense map management component determinesnot to move to the merged or stitched dense map, the device would notfetch the dense map data.

When the dense map management component determines to move to the mergedor stitched dense map, the device may proceed to fetch the dense mapdata from the cloud. The merged or stitched dense map may be dividedinto smaller volumes (e.g., blocks) because the merged or stitched densemap can be potentially very large. When fetching and loading the mergedor stitched dense map, the device may first fetch and load those dataclose to the device (e.g., data that are 100 meters away have a lowerpriority than data that are 5 meters away). As a user wearing the devicemoves around, the device may continuously fetch and load datacorresponding to an area visible by the device. In some embodiments, thedevice may fetch some data in advance based on, for example, predictedmotion of the device such that data may be ready for loading when thedevice arrives at locations and the latency caused by fetching data fromthe cloud may be reduced.

A dense map merge or stitch component of the device may update portionsof the merged or stitched dense map that have been loaded with the freshlocal posed sensor data. When the device receives a new merged orstitched dense map from the cloud, the device may have fresh local posedsensor data that are not part of the merged or stitched dense mapbecause the device captures sensor data in parallel to the computationshappening on the cloud. Updating portions of the merged or stitcheddense map prevents the device from losing this fresh data.

A dense map merge or stitch component of the device may alsocontinuously update and correct persistent poses in the sparse trackingmap based on the fresh local posed sensor data. A 3D reconstructiongenerated using posed sensor data tied to the updated persistent posesmay similarly be updated based on any updates to the persistent poses.

FIG. 69 is a block diagram of an example of the on-device dense maphandling component 6900, according to some embodiments. The on-devicedense map handling component 6900 may include dense map localizationcomponent 6904, dense map relocalization component 6916, dense mapmanagement component 6906, dense map pre-fetch component 6910, and densemap merge or stitch component 6912. In some embodiments, the on-devicedense map handling component 6900 may determine whether to enable thedense map localization component 6904 or the dense map relocalizationcomponent 6916 based on the sparse map localization results 6714.

The dense map localization component 6904 may be enabled when the sparsemap localization results 6714 indicates that the device localizes to anew shared sparse map. The dense map localization component 6904 mayselect a shared dense map against which the device is to localize. Acriterion in selection of a shared map may be overlap in coverage of theselected dense map and a shared sparse map to which the device haslocalized, where overlap may be determined by persistent data in commonbetween the two maps. Other criteria may also be applied if there ismore than one shared dense map with overlapping coverage, such asquality of the dense map that the device currently localizes to, andtimestamp of the current dense map. Quality of a dense map may indicatethe number of mesh blocks in the map. Timestamp of a dense map mayindicate the time when the last depth image is fused into the dense mapand/or the time when the map is created on the cloud.

A device may attempt to get new localization results periodically, forexample, when a device moves 10 meters from its previous localizedposition so as to reduce errors accumulated in the device's trajectory.As a sparse map may cover a larger area, the sparse map localizationresults 6714 may often indicate that the device localizes to the sameshared sparse map. The dense map relocalization component 6906 may beenabled to update the transformations between the device's sparsetracking map and the shared sparse map such that misalignments betweenthe maps are reduced.

Based on metadata 6908 of the localized shared dense map, the dense mappre-fetch component 6910 may proceed to fetch portions of the localizedshared dense map by, for example, performing a method 7000 illustratedin FIG. 70. FIG. 71 is a simplified schematic diagram illustratingobtaining the merged or stitched dense map 6800 using the method 7000,according to some embodiments.

The method 7000 may begin when a dense map management componentdetermines that a device can localize to a shared dense map from thecloud, which may trigger dense map pre-fetching.

The method 7000 may include computing (act 7004) a current prefetchregion 7106. Based on the device's current position (e.g., position7102). Some or all of the prefetch data may be a load region (e.g.,bounding box 7104), loaded into active RAM on the device. In someembodiments, for example, a device may maintain data in active RAMcorresponding to a load region that is smaller than the prefetch region.All or a portion of the prefetch data may be stored in other memory,such as a file system implemented in solid state non-volatile memory.The load region may be changed as the device's position 7102 changes.

In some embodiments, the prefetch data may be downloaded incrementally.The method 7000 may include sending (act 7006) prefetch requests for aportion of the subregions for which the device does not already storedata, with preference being given for data in the current load region.For example, subregions within the bounding box 7104 may be firstrequested. The requested portion of the subregions may be marked as“requested.”

The method 7000 may include receiving (act 7008) responses to theprefetch requests. The responses may include data of the shared densemap such as volumetric data, meshes, and objects information. Thereceived prefetch region may be marked as “completed.” The prefetchregion that are marked as “completed” may be loaded to the device'smemory for XR functions such as occlusion, physics-based interactions,and environmental reasoning.

After certain criteria are met, such as the mesh blocks in the loadregion are all downloaded and loaded to RAM, a dense map managementcomponent may localize to this previously determined shared dense map.Thereafter, based on current location and detected motion of the device,a dense map pre-fetch component may continue to fetch and load data fromthe cloud to device RAM. As a result, the method 7000 may iterate theacts 7004, 7006, and 7008 based on, for example, the device's currentlocation and any detected motion of the device. In some embodiments,after fetching all subregions in the bounding box 7104, the method 7000may compute a new current prefetch region by, for example, enlarging thebounding box 7104 (e.g., the enlarged bounding box and/or shifting thebounding box 7104 (e.g., pre-fetch box 7108).

The fetched portions of the shared dense map may be loaded to thedevice's memory when the device enters into corresponding area in thephysical environment. The dense map merge or stitch component 6912 mayupdate the loaded portions of the shared dense map by, for example,performing a method 7200 illustrated in FIG. 72.

The method 7200 may start by checking (act 7202) timestamp of the lastupdated to the localized shared dense map. The method 7200 may includequeuing (act 7204) persistent poses from the sparse tracking map withthe corresponding depth images captured after the timestamp.

For each depth image in the queue, the method 7200 may include checking(act 7206) a distance between the device's current location and apersistent pose that the depth image is attached to. The method 7200 mayinclude determining (act 7208) whether the distance is within athreshold (e.g., 15 meters). When it is determined that the distance iswithin the threshold value, the method 7200 may include querying (act7210) all depth images associated with these persistent poses from thecloud's passable world, which may have been downloaded and storedlocally on the device. The method 7200 may include integrating (act7212) the depth image in the queue with the queried depth images fromthe cloud's passable world. This persistent pose may be deleted from thequeue after the integration. When it is determined that the distance isabove the threshold value, the persistent pose may be deleted from thequeue. The method 7200 may include determining (act 7214) whether thereare additional persistent poses in the queue. The acts 7206-7214 mayiterate until no persistent poses are in the queue.

Geometries such as planar surfaces may be extracted as an XR devicemoves around. Extracting geometries takes less computing power and timethan building a complete dense map. Simple geometries may be efficientand sufficient for some XR functions. The extracted geometries may bepromoted to the cloud such that the geometries can be shared with anydevices in the XR device as well as any virtual content attached to theshared geometries.

In some embodiments, an on-device 3D reconstruction component (e.g.,64360 may include a geometry extraction component. The geometryextraction component may extract geometries while scanning a scene withsensors, which allows a fast, efficient extraction that can accommodatedynamic environment changes. The extracted geometries may be stored onthe device and/or promoted to the cloud, for example, as at least aportion of objects information of a corresponding dense map (e.g.,objects information 6438, objects information 6448). The cloud mayinclude one or more components configured to combine geometries providedby one or more devices. Each of the combined geometries on the cloud mayhave a unique geometry identifier such that the combined geometries andvirtual content attached hereby can be shared by devices in the XRsystem.

In some embodiments, objects may be individually identifiable, and theseidentifiers may be persistent across sessions, in the same way aspersistent poses. With individual identifiers, objects can be referencedindependently and are distinguishable from each other. They can beclassified as different instances of various types. For example, a largeflat wall surface may have the same identifier over time. Conversely,the same identifier over time may refer to the same flat wall surface.The ability to refer to persistent objects may simplify processing, suchas content placement. For example, a virtual screen may be placed on alarge flat wall surface by referencing it via its identifier, withoutworrying that this identifier may point to a different entity later,e.g., the ceiling instead.

Although planes are used as an exemplary geometry in some embodiments,it should be appreciated that a geometry extraction component may detectother geometries to use in subsequent processing instead of or inaddition to planes, including, for example, cylinders, cubes, lines,corners, or semantics such as glass surfaces or holes. In someembodiments, the principles described herein with respect to geometryextraction may be applicable to object extraction and the like.

In some embodiments, a transformation may be obtained for transformingobject information (e.g., 6438) from a device in a coordinate framelocal to the device to the shared coordinate frame of cloud merged orstitched sparse maps. In some embodiments, the transformation may use asparse map localization result. For example, for the dense maps thatcontain uploaded planar surfaces, the cloud may find the correspondinglocal tracking sparse map using the same map unique identifier, andperform sparse map localization from this sparse map to the cloud mergedor stitched sparse map. A pose may be obtained from the local trackingsparse map to the cloud merged or stitched sparse map, and used as anadditional input for processing object information. In some embodiments,for dense maps that contain uploaded planar surfaces, the cloud may findthe corresponding local tracking sparse map using the same map uniqueidentifier, and extract a persistent pose table to be used as anadditional input for processing object information. Each plane (whichmay be identified by its unique identifier) may be attached to apersistent pose in the persistent pose table of its local trackingsparse map. By finding the same persistent pose in the cloud merged orstitched sparse map, a pose may be obtained from the local trackingsparse map to the cloud merged or stitched sparse map for this plane.

The relative pose of the between the local map and the cloud map towhich to the same object (such as a plane) is posed may serve as atransformation. Based on the obtained transformation, the objectinformation from the device may be transformed from the coordinate framelocal to the device to the shared coordinate frame of the cloud mergedor stitched sparse maps by, for example, using poses and/or performinggeometry-based matching such as bounding box matching.

An object UUID transfer mapping may be computed and accessible bydevices in the XR system. The object UUID transfer mapping may indicatecorrespondences between cloud object information UUIDs to input objectinformation UUIDs. The object UUID transfer mapping may enable devicesto recognize persistent objects that have previously been seen by thedevice through, for example, UUIDs.

A device in the XR system may load a merged or stitched dense map, theobject UUIDs may be those UUIDs in the merged or stitched dense map,which may be mapped back to the UUIDs the device has seen before basedon the object UUID transfer mapping, and thus provide persistency fromthe device's viewpoint.

FIG. 73 is a block diagram of an XR system 7300 that provides persistentobjects, such as planes, according to some embodiments. The XR system7300 may include a cloud 7302 and one or more devices 7304. Objects maybe persistent across multiple devices, for example, in embodiments inwhich surface information uploaded to the cloud includes object info(e.g., planes), so that the cloud components can match objects betweenthe local dense maps, which contain object information, and cloud shareddense maps, which may also contain object information. As a specificexample, the XR system 7300 may include a plane matching component 7600,which may be part of the devices 7304 and/or the cloud 7302.

The device 7304 may include one or more components configured to extractplanes by, for example, detecting planar surfaces from meshes; to mergeor stitch planes into one global plane when, for example, a newextracted plane connects two planes; and to split a global plane when,for example, a brick plane in the middle of the global plane is removed.For example, examples of plane extraction system are described in U.S.patent application Ser. No. 16/229,799, which is hereby incorporatedherein by reference it its entirety.

The device 7304 may include memory 7312 and filesystem 7314. The memory7312 may include local plane data 7316 and local plane ID history map7318. The local plane data 7316 may include plane information such asboundary points of a plane, an area of a plane, and a primitive normalof a plane. For a queried plane, the plane ID history map 7318 mayinclude correspondences between the queried plane's unique ID to anyhistorical IDs of the plane. The historical IDs may indicate thetimestamp that at least a portion of the plane was last queried acrossmultiple sessions.

The memory 7312 and the filesystem 7314 may operate in cooperation suchthat shared and local geometries can be stored in the filesystem toavoid duplicated environment mapping when entering a previously exploredspace. In the illustrated example, portions of the local plane data 7316and local plane ID history map 7318 may be retrieved from the filesystem7314 based on the device's current location. When the device moves awayfrom some real objects in the physical environment, the device 7304 mayremove corresponding plane data and its ID history map from the memory7312, and store them in the filesystem 7314. In some embodiments,although the data in the memory 7312 may be removed after the end of asession, the data in the filesystem 7314 may be persisted and ready tobe loaded to the memory 7312 upon the start of a new session.

FIG. 74 is a block diagram of an XR system 7400 that providespersistence to objects, such as planes, according to some embodiments.The XR system 7400 may include a cloud 7402 and one or more devices7404. Cloud 7402 may be implemented with the same processing resourcesthat perform cloud based sparse map localization, as described above, ormay be other compute resources. The XR system 7400 may include a planematching component 7406. Although the plane matching component 7406 isillustrated as part of the cloud 7402, it may also be part of thedevices 7404, or may be only part of the devices 7404 or only part ofthe cloud 7402.

The cloud 7402 may include shared sparse map 7412 that includepersistent pose table, and shared dense map 7414 that include planes7416 and a local to cloud plane UUID mapping 7418. The plane matchingcomponent 7406 may provide the local to cloud plane UUID mapping 7418based, at least in part, on the sparse map 7412 of the cloud 7402 andplanes 7416 of the dense map 7414.

The device 7404 may include sparse tracking map 7422 that includepersistent pose table, and dense map 7424 that include planes 7426 and alocal to cloud plane UUID mapping 7428. The local to cloud plane UUIDmapping 7428 may be from a shared cloud dense map loaded by the device7404 from the cloud 7402, for example, the local to cloud plane UUIDmapping 7418. The plane matching component 7406 may provide the local tocloud plane UUID mapping 7418 based, at least in part, on the sparse map7422 of the device 7404 and planes 7426 of the dense map 7424.

In this example embodiment, as devices provide dense informationincluding persistent objects, such as planes, plane matching component7406 may attempt to match the planes from the device to planes 7426stored in the cloud. If the plane from the device has already beenassociated with a plane stored in the cloud, its identifier may bestored in mapping 7418, enabling matching to be performed based onidentifiers.

If the plane from the device is not in mapping 7418, matching may bebased on plane geometry and other information about the planes. Forexample, match processing may be based on persistent pose to which theplanes are posed as well as the pose. Overlapping planes may be treatedas matching. Upon finding a match, the association between theidentifier for plane used by the device may be recorded in mapping 7418and communicated to the device for storage in mapping 7428.

If no matching plane is identified, in some embodiments, the plane fromthe device may be added to the cloud store of share planes 7414 andassigned a cloud identifier. The mapping between the cloud identifiermay then be stored in mapping 7418 and may likewise be communicated tothe device to be stored in mapping 7428. In this way, planes, or otherobjects, may be identified on each device that interacts with the systemas well as in the cloud.

It should also be appreciated that, in addition to adding surfaceinformation based on dense information from devices, cloud processingmay remove or update dense information. For example, as indicated inFIG. 67, a device may maintain a current dense map 6708. That dense mapmay be expanded as more sensor data is collected and processed on thedevice. Updates that extend the dense map may be communicated to thecloud for merging or stitching with dense maps in the cloud. Conversely,when updates on a device indicate that a previously detected surface orobject is no longer present, the update may result in removing surfaceinformation from the cloud.

Similarly, as a device operates its sensors to gather information aboutits 3D environment, the device may, from time to time, adjust itsrepresentation of the 3D environment. In some embodiments, a device mayfrom time to time adjust its sparse tracking map, such as may occurduring a bundle adjustment as described above. An adjustment of thesparse tracking map may trigger an adjustment to the dense informationthat was posed relative to the tracking map. For example, the sensordata used to generate dense information may be posed relative to thepersistent poses in the tracking map such that an adjustment of thetracking map may result in an adjustment of the sensor data. Adjustedsurface information may then be generated. The adjusted surfaceinformation may replace similar surface information generated with theun-adjusted surface information. This replacement may be made on surfaceinformation local to the device as well as on the cloud.

Accordingly, a reliable representation of a 3D environment in which eachof multiple devices operates may be generated and maintained, enablingthe benefits of persistence with low on-device resources.

Further Considerations

FIG. 60 shows a diagrammatic representation of a machine in theexemplary form of a computer system 1900 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed, according to someembodiments. In alternative embodiments, the machine operates as astandalone device or may be connected (e.g., networked) to othermachines. Further, while only a single machine is illustrated, the term“machine” shall also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

The exemplary computer system 1900 includes a processor 1902 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 1904 (e.g., read only memory (ROM), flash memory,dynamic random-access memory (DRAM) such as synchronous DRAM (SDRAM) orRambus DRAM (RDRAM), etc.), and a static memory 1906 (e.g., flashmemory, static random-access memory (SRAM), etc.), which communicatewith each other via a bus 1908.

The computer system 1900 may further include a disk drive unit 1916, anda network interface device 1920.

The disk drive unit 1916 includes a machine-readable medium 1922 onwhich is stored one or more sets of instructions 1924 (e.g., software)embodying any one or more of the methodologies or functions describedherein. The software may also reside, completely or at least partially,within the main memory 1904 and/or within the processor 1902 duringexecution thereof by the computer system 1900, the main memory 1904 andthe processor 1902 also constituting machine-readable media.

The software may further be transmitted or received over a network 18via the network interface device 1920.

The computer system 1900 includes a driver chip 1950 that is used todrive projectors to generate light. The driver chip 1950 includes itsown data store 1960 and its own processor 1962.

While the machine-readable medium 1922 is shown in an exemplaryembodiment to be a single medium, the term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” shall also be taken to include any medium thatis capable of storing, encoding, or carrying a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present invention. The term“machine-readable medium” shall accordingly be taken to include, but notbe limited to, solid-state memories, optical and magnetic media, andcarrier wave signals.

Having thus described several aspects of some embodiments, it is to beappreciated that various alterations, modifications, and improvementswill readily occur to those skilled in the art.

As one example, embodiments are described in connection with anaugmented (AR) environment. It should be appreciated that some or all ofthe techniques described herein may be applied in an MR environment ormore generally in other XR environments, and in VR environments.

As another example, embodiments are described in connection withdevices, such as wearable devices. It should be appreciated that some orall of the techniques described herein may be implemented via networks(such as cloud), discrete applications, and/or any suitable combinationsof devices, networks, and discrete applications.

Further, FIG. 29 provides examples of criteria that may be used tofilter candidate maps to yield a set of high-ranking maps. Othercriteria may be used instead of or in addition to the describedcriteria. For example, if multiple candidate maps have similar values ofa metric used for filtering out fewer desirable maps, characteristics ofthe candidate maps may be used to determine which maps are retained ascandidate maps or filtered out. For example, larger or more densecandidate maps may be prioritized over smaller candidate maps.

It shall be noted that any obvious alterations, modifications, andimprovements are contemplated and intended to be part of thisdisclosure, and are intended to be within the spirit and scope of thedisclosure. Further, though advantages of the present disclosure areindicated, it should be appreciated that not every embodiment of thedisclosure will include every described advantage. Some embodiments maynot implement any features described as advantageous herein and in someinstances. Accordingly, the foregoing description and drawings are byway of example only.

The above-described embodiments of the present disclosure can beimplemented in any of numerous ways. For example, the embodiments may beimplemented using hardware, software or a combination thereof. Whenimplemented in software, the software code can be executed on anysuitable processor or collection of processors, whether provided in asingle computer or distributed among multiple computers. Such processorsmay be implemented as integrated circuits, with one or more processorsin an integrated circuit component, including commercially availableintegrated circuit components known in the art by names such as CPUchips, GPU chips, microprocessor, microcontroller, or co-processor. Insome embodiments, a processor may be implemented in custom circuitry,such as an ASIC, or semicustom circuitry resulting from configuring aprogrammable logic device. As yet a further alternative, a processor maybe a portion of a larger circuit or semiconductor device, whethercommercially available, semi-custom or custom. As a specific example,some commercially available microprocessors have multiple cores suchthat one or a subset of those cores may constitute a processor. Though,a processor may be implemented using circuitry in any suitable format.

Further, it should be appreciated that a computer may be embodied in anyof a number of forms, such as a rack-mounted computer, a desktopcomputer, a laptop computer, or a tablet computer. Additionally, acomputer may be embedded in a device not generally regarded as acomputer but with suitable processing capabilities, including a PersonalDigital Assistant (PDA), a smart phone or any other suitable portable orfixed electronic device.

Also, a computer may have one or more input and output devices. Thesedevices can be used, among other things, to present a user interface.Examples of output devices that can be used to provide a user interfaceinclude printers or display screens for visual presentation of outputand speakers or other sound generating devices for audible presentationof output. Examples of input devices that can be used for a userinterface include keyboards, and pointing devices, such as mice, touchpads, and digitizing tablets. As another example, a computer may receiveinput information through speech recognition or in other audible format.In the embodiment illustrated, the input/output devices are illustratedas physically separate from the computing device. In some embodiments,however, the input and/or output devices may be physically integratedinto the same unit as the processor or other elements of the computingdevice. For example, a keyboard might be implemented as a soft keyboardon a touch screen. In some embodiments, the input/output devices may beentirely disconnected from the computing device, and functionallyintegrated through a wireless connection.

Such computers may be interconnected by one or more networks in anysuitable form, including as a local area network or a wide area network,such as an enterprise network or the Internet. Such networks may bebased on any suitable technology and may operate according to anysuitable protocol and may include wireless networks, wired networks orfiber optic networks.

Also, the various methods or processes outlined herein may be coded assoftware that is executable on one or more processors that employ anyone of a variety of operating systems or platforms. Additionally, suchsoftware may be written using any of a number of suitable programminglanguages and/or programming or scripting tools, and also may becompiled as executable machine language code or intermediate code thatis executed on a framework or virtual machine.

In this respect, the disclosure may be embodied as a computer readablestorage medium (or multiple computer readable media) (e.g., a computermemory, one or more floppy discs, compact discs (CD), optical discs,digital video disks (DVD), magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other tangible computer storage medium) encoded with one ormore programs that, when executed on one or more computers or otherprocessors, perform methods that implement the various embodiments ofthe disclosure discussed above. As is apparent from the foregoingexamples, a computer readable storage medium may retain information fora sufficient time to provide computer-executable instructions in anon-transitory form. Such a computer readable storage medium or mediacan be transportable, such that the program or programs stored thereoncan be loaded onto one or more different computers or other processorsto implement various aspects of the present disclosure as discussedabove. As used herein, the term “computer-readable storage medium”encompasses only a computer-readable medium that can be considered to bea manufacture (i.e., article of manufacture) or a machine. In someembodiments, the disclosure may be embodied as a computer readablemedium other than a computer-readable storage medium, such as apropagating signal.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that can be employed to program a computer or otherprocessor to implement various aspects of the present disclosure asdiscussed above. Additionally, it should be appreciated that accordingto one aspect of this embodiment, one or more computer programs thatwhen executed perform methods of the present disclosure need not resideon a single computer or processor, but may be distributed in a modularfashion amongst a number of different computers or processors toimplement various aspects of the present disclosure.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically, the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in anysuitable form. For simplicity of illustration, data structures may beshown to have fields that are related through location in the datastructure. Such relationships may likewise be achieved by assigningstorage for the fields with locations in a computer-readable medium thatconveys relationship between the fields. However, any suitable mechanismmay be used to establish a relationship between information in fields ofa data structure, including through the use of pointers, tags or othermechanisms that establish relationship between data elements.

Various aspects of the present disclosure may be used alone, incombination, or in a variety of arrangements not specifically discussedin the embodiments described in the foregoing and is therefore notlimited in its application to the details and arrangement of componentsset forth in the foregoing description or illustrated in the drawings.For example, aspects described in one embodiment may be combined in anymanner with aspects described in other embodiments.

Also, the disclosure may be embodied as a method, of which an examplehas been provided. The acts performed as part of the method may beordered in any suitable way. Accordingly, embodiments may be constructedin which acts are performed in an order different than illustrated,which may include performing some acts simultaneously, even though shownas sequential acts in illustrative embodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed, but are usedmerely as labels to distinguish one claim element having a certain namefrom another element having a same name (but for use of the ordinalterm) to distinguish the claim elements.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

What is claimed is:
 1. An extended reality or cross reality (XR) systemfor rendering virtual content, comprising: a computing devicecommunicably connected to a plurality of portable electronic devices viaa network component; a repository accessible by the computing device andthe plurality of portable electronic devices; and a dense map mergecomponent, the extended reality or cross reality system configured toperform a set of acts that comprises: determining a representation formultiple portions of a 3D environment based at least in part upon on aset of dense maps received from the plurality of portable devices,wherein the set of dense maps is grouped into multiple subgroups basedat least in part upon pose data pertaining to the set of dense maps orsurface information in the set of dense maps, and storing therepresentation as at least a portion of a shared persistent dense map.2. The extended reality or cross reality (XR) system of claim 1, the setof acts further comprising: localizing a sparse map and a dense map to acanonical map that is selected from multiple canonical maps stored in arepository, wherein a coordinate system is derived from the sparse mapand is used to determine a position or an orientation of an object inthe dense map.
 3. The extended reality or cross reality (XR) system ofclaim 2, the set of acts further comprising: determining, for acanonical map, the sparse map comprising headpose data of a headpose ofa portable electronic device and object information for one or moreobjects detected by the portable electronic device at the headposes,wherein the sparse map is oriented local to a local reference frame forthe portable electronic device.
 4. The extended reality or cross reality(XR) system of claim 2, the set of acts further comprising: determiningthe dense map in a first processing pipeline, wherein the sparse map isdetermined in a second processing pipeline and is refreshed at a secondrefresh rate, the dense map is refreshed at a first rate slower than thesecond refresh rate, and the dense map comprises dense information thatreferences to a persistent pose of a sparse map.
 5. The extended realityor cross reality (XR) system of claim 2, the set of acts furthercomprising: promoting the sparse map to a canonical map based at leastin part upon a condition that determines an extent of overlap betweenthe sparse map and one or more canonical maps that have been stored inthe repository.
 6. The extended reality or cross reality (XR) system ofclaim 1, wherein the set of acts further comprising: generating, at theportable electronic device, a shared world model at least by performinga local 3D reconstruction.
 7. The extended reality or cross reality (XR)system of claim 6, generating the shared world model at least byperforming the local 3D reconstruction comprising: identifying, at theportable electronic device of the plurality of portable electronicdevices, a pose of a sparse map; and identifying a shared persistentdense map from a repository accessible by the computing device.
 8. Theextended reality or cross reality (XR) system of claim 7, generating theshared world model at least by performing the local 3D reconstructioncomprising: localizing the sparse map generated by the portableelectronic device to a canonical map at least by using a transform, thetransform determined for the sparse map and the canonical map based atleast in part upon one or more persistent coordinate frames in thecanonical map.
 9. The extended reality or cross reality (XR) system ofclaim 8, generating the shared world model at least by performing thelocal 3D reconstruction comprising: generating, at the portableelectronic device, a dense map at least by determining a local pose dataor local depth data of the object or a portion thereof from an imagecaptured by the portable electronic device.
 10. The extended reality orcross reality (XR) system of claim 8, wherein the dense map is generatedat the portable electronic device at least further by identifyinglocalized pose data or localized depth data relative to the canonicalmap, the localized pose data or localized depth data respectivelytransformed, by the transform, from the localized pose data or localdepth data, and transmitting the localized pose data or the localizeddepth data.
 11. The extended reality or cross reality (XR) system ofclaim 1, wherein the set of acts further comprising: performing, at thedense map merge component residing on a remote server, 3D reconstructionof the 3D environment.
 12. The extended reality or cross reality (XR)system of claim 1, performing the 3D reconstruction of the 3Denvironment comprising: receiving, from a portable electronic device ofthe plurality of portable electronic devices, local pose data of asparse map generated by the portable electronic device; and identifyinga shared persistent dense map from a repository accessible by the densemap merge component.
 13. The extended reality or cross reality (XR)system of claim 12, performing the 3D reconstruction of the 3Denvironment further comprising: determining a transform that localizesthe sparse map generated by the portable electronic device to acanonical map, wherein the transform is determined for the sparse mapand the canonical map based at least in part upon one or more persistentcoordinate frames in the canonical map and pose data in the sparse map.14. The extended reality or cross reality (XR) system of claim 13,performing the 3D reconstruction of the 3D environment furthercomprising: receiving, from the portable electronic device, a dense map,wherein the dense map is generated by using surface at least a portionof the collections of posed surface information and a local pose data orlocal depth data pertaining to an object or a portion thereof in animage captured by the portable electronic device, the local pose dataand the local depth data are respectively transformed into localizedpose data or localized depth data relative to the canonical map;identifying the dense map that corresponds to the sparse map based atleast in part upon a localization result of localizing the sparse map tothe canonical map; and computing the representation of the 3Denvironment at least by merging the dense map to the shared persistentdense map as a part of the representation.
 15. The extended reality orcross reality (XR) system of claim 14, the set of acts furthercomprising: localizing a sparse map generated by the portable electronicdevice of the plurality of portable electronic device to the canonicalmap; and generating, at a sparse map merge component, a sharedpersistent sparse map for the 3D environment.
 16. The extended realityor cross reality (XR) system of claim 15, generating the sharedpersistent sparse map comprising: receiving, from a portable electronicdevice of the plurality of portable electronic devices, local pose dataof the object or the portion thereof in a sparse map generated by theportable electronic device, wherein the local pose data is oriented inrelation to the portable electronic device, wherein the object or theportion thereof is represented as a point node in the sparse map; andidentifying at least one persistent coordinate frame for the sparse mapfrom the canonical map based at least in part upon the local pose dataof the object or the portion thereof.
 17. The extended reality or crossreality (XR) system of claim 16, generating the shared persistent sparsemap comprising: localizing the sparse map to the canonical map at leastby transforming the local pose data into localized pose data based atleast in part upon the at least one persistent coordinate frame storedin the canonical map; and generating the shared persistent sparse mapfor the 3D environment at least by merging the sparse map into theshared persistent sparse map.
 18. A computer program product comprisingone or more non-transitory computer readable storage media having storedthereupon at least one sequence of instructions which, when executed byan extended reality or cross reality (XR) system, causes the extendedreality or cross reality system to perform a set of acts, the set ofacts comprising: determining a repository accessible by the extendedreality or cross reality system, the repository storing therein multiplecanonical maps, wherein a canonical map stores one or more persistentcoordinate frames corresponding to at least one object or one or moreportions thereof; determining a representation for multiple portions ofa 3D environment based at least in part upon on a set of dense mapsreceived from the plurality of portable devices, wherein the set ofdense maps is grouped into multiple subgroups based at least in partupon pose data pertaining to the set of dense maps or surfaceinformation in the set of dense maps, and storing the representation asat least a portion of a shared persistent dense map.
 19. The computerprogram product of claim 18, the set of acts further comprising:localizing a sparse map and a dense map to a canonical map that isselected from multiple canonical maps stored in a repository, wherein acoordinate system is derived from the sparse map and is used todetermine a position or an orientation of an object in the dense map;determining, for a canonical map, the sparse map comprising headposedata of a headpose of a portable electronic device and objectinformation for one or more objects detected by the portable electronicdevice at the headposes, wherein the sparse map is oriented local to alocal reference frame for the portable electronic device; determiningthe dense map in a first processing pipeline, wherein the sparse map isdetermined in a second processing pipeline and is refreshed at a secondrefresh rate, the dense map is refreshed at a first rate slower than thesecond refresh rate, and the dense map comprises dense information thatreferences to a persistent pose of a sparse map; and promoting thesparse map to a canonical map based at least in part upon a conditionthat determines an extent of overlap between the sparse map and one ormore canonical maps that have been stored in the repository.
 20. Thecomputer program product of claim 18, the set of acts furthercomprising: generating, at the portable electronic device, a sharedworld model at least by performing a local 3D reconstruction at leastby: identifying, at the portable electronic device of the plurality ofportable electronic devices, a pose of a sparse map; identifying ashared persistent dense map from a repository accessible by thecomputing device; localizing the sparse map generated by the portableelectronic device to a canonical map at least by using a transform, thetransform determined for the sparse map and the canonical map based atleast in part upon one or more persistent coordinate frames in thecanonical map; and generating, at the portable electronic device, adense map at least by determining a local pose data or local depth dataof the object or a portion thereof from an image captured by theportable electronic device.
 21. An electronic device for rendering crossreality representation, the electronic device comprising: one or moresensors configured to capture information about a three-dimensional (3D)environment, the information comprising a plurality of images; aprocessor configured to execute a sequence of instructions, wherein thesequence of instructions, when executed, causes the processor to performa set of acts, the set of acts comprising: computing a sparse trackingmap based at least in part on the plurality of images; determining oneor more collections of surface or depth information based at least inpart on the information captured by the one or more sensors;transmitting at least one of the one or more collections of surface ordepth information and pose information for the at least one of the oneor more collections of surface or depth information; receiving metadataof a dense map, the metadata indicating a portion of the 3D environmentrepresented by the dense map; and determining, based at least in part onthe sparse tracking map and the metadata, whether to retrieve at least aportion of the dense map.
 22. The electronic device of claim 21, whereinthe metadata of the dense map comprises a quality metric, whichindicates a number of mesh blocks in the dense map, and a timestampindicating the time when a last depth image has been fused into thedense map, and determining whether to obtain at least a portion of thedense map is based at least in part on the quality metric of the densemap and quality metrics of one or more dense different maps.
 23. Theelectronic device of claim 21, further comprising: computing a locallymerged map based at least in part on at least a portion of the dense mapand the at least one of the one or more collections of surface or depthinformation, wherein the at least one of the one or more collections ofsurface or depth information is not represented in the at least theportion of the dense map.
 24. The electronic device of claim 21, the setof acts further comprising: determining an area into which theelectronic device is moving, based at least in part on a pose of theelectronic device, and downloading surface information of the area whenit is determined that the surface information of the area is availablein the dense map but not on the device, wherein the dense map comprisesa plurality of sub-regions associated with persistent poses in a sparsetracking map, and one or more sub-regions corresponding to the area aredownloaded in an order based at least in part on respective distancesbetween a pose of the electronic device and one or more persistent posesassociated with the sub-regions.